text:
@inproceedings{turney_thumbs_2002,
  address = {Stroudsburg, PA, USA},
  series = {ACL '02},
  title = {Thumbs {{Up}} or {{Thumbs Down}}?: {{Semantic Orientation Applied}} to {{Unsupervised Classification}} of {{Reviews}}},
  shorttitle = {Thumbs {{Up}} or {{Thumbs Down}}?},
  doi = {10.3115/1073083.1073153},
  abstract = {This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74\% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84\% for automobile reviews to 66\% for movie reviews.},
  booktitle = {Proceedings of the 40th {{Annual Meeting}} on {{Association}} for {{Computational Linguistics}}},
  publisher = {{Association for Computational Linguistics}},
  author = {Turney, Peter D.},
  year = {2002},
  pages = {417--424},
  file = {/Users/Brad/Zotero/storage/7F3FPFFW/Turney - 2002 - Thumbs Up or Thumbs Down Semantic Orientation Ap.pdf}
}

@inproceedings{hu_mining_2004,
  address = {New York, NY, USA},
  series = {KDD '04},
  title = {Mining and {{Summarizing Customer Reviews}}},
  isbn = {978-1-58113-888-7},
  doi = {10.1145/1014052.1014073},
  abstract = {Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.},
  booktitle = {Proceedings of the {{Tenth ACM SIGKDD International Conference}} on {{Knowledge Discovery}} and {{Data Mining}}},
  publisher = {{ACM}},
  author = {Hu, Minqing and Liu, Bing},
  year = {2004},
  keywords = {reviews,sentiment classification,summarization,text mining},
  pages = {168--177},
  file = {/Users/Brad/Zotero/storage/Q974VPSH/Hu and Liu - 2004 - Mining and Summarizing Customer Reviews.pdf}
}

@inproceedings{morinaga_mining_2002,
  address = {New York, NY, USA},
  series = {KDD '02},
  title = {Mining {{Product Reputations}} on the {{Web}}},
  isbn = {978-1-58113-567-1},
  doi = {10.1145/775047.775098},
  abstract = {Knowing the reputations of your own and/or competitors' products is important for marketing and customer relationship management. It is, however, very costly to collect and analyze survey data manually. This paper presents a new framework for mining product reputations on the Internet. It automatically collects people's opinions about target products from Web pages, and it uses text mining techniques to obtain the reputations of those products.On the basis of human-test samples, we generate in advance syntactic and linguistic rules to determine whether any given statement is an opinion or not, as well as whether such any opinion is positive or negative in nature. We first collect statements regarding target products using a general search engine, and then, using the rules, extract opinions from among them and attach three labels to each opinion, labels indicating the positive/negative determination, the product name itself, and an numerical value expressing the degree of system confidence that the statement is, in fact, an opinion. The labeled opinions are then input into an opinion database.The mining of reputations, i.e., the finding of statistically meaningful information included in the database, is then conducted. We specify target categories using label values (such as positive opinions of product A) and perform four types of text mining: extraction of 1) characteristic words, 2) co-occurrence words, 3) typical sentences, for individual target categories, and 4) correspondence analysis among multiple target categories.Actual marketing data is used to demonstrate the validity and effectiveness of the framework, which offers a drastic reduction in the overall cost of reputation analysis over that of conventional survey approaches and supports the discovery of knowledge from the pool of opinions on the web.},
  booktitle = {Proceedings of the {{Eighth ACM SIGKDD International Conference}} on {{Knowledge Discovery}} and {{Data Mining}}},
  publisher = {{ACM}},
  author = {Morinaga, Satoshi and Yamanishi, Kenji and Tateishi, Kenji and Fukushima, Toshikazu},
  year = {2002},
  pages = {341--349},
  file = {/Users/Brad/Zotero/storage/UHKVCBE3/Morinaga et al. - 2002 - Mining Product Reputations on the Web.pdf}
}

@inproceedings{popescu_extracting_2005,
  address = {Stroudsburg, PA, USA},
  series = {HLT '05},
  title = {Extracting {{Product Features}} and {{Opinions}} from {{Reviews}}},
  doi = {10.3115/1220575.1220618},
  abstract = {Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces Opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products.Compared to previous work, Opine achieves 22\% higher precision (with only 3\% lower recall) on the feature extraction task. Opine's novel use of relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.},
  booktitle = {Proceedings of the {{Conference}} on {{Human Language Technology}} and {{Empirical Methods}} in {{Natural Language Processing}}},
  publisher = {{Association for Computational Linguistics}},
  author = {Popescu, Ana-Maria and Etzioni, Oren},
  year = {2005},
  pages = {339--346},
  file = {/Users/Brad/Zotero/storage/MXKVWSHX/Popescu and Etzioni - 2005 - Extracting Product Features and Opinions from Revi.pdf}
}

@article{turney_measuring_2003,
  title = {Measuring {{Praise}} and {{Criticism}}: {{Inference}} of {{Semantic Orientation}} from {{Association}}},
  volume = {21},
  issn = {1046-8188},
  shorttitle = {Measuring {{Praise}} and {{Criticism}}},
  doi = {10.1145/944012.944013},
  abstract = {The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8\% on the full test set, but the accuracy rises above 95\% when the algorithm is allowed to abstain from classifying mild words.},
  number = {4},
  journal = {ACM Trans. Inf. Syst.},
  author = {Turney, Peter D. and Littman, Michael L.},
  month = oct,
  year = {2003},
  keywords = {text mining,latent semantic analysis,mutual information,semantic association,semantic orientation,text classification,unsupervised learning,web mining},
  pages = {315--346},
  file = {/Users/Brad/Zotero/storage/2JGJHMBZ/Turney and Littman - 2003 - Measuring Praise and Criticism Inference of Seman.pdf}
}

@article{cambria_new_2013,
  title = {New {{Avenues}} in {{Opinion Mining}} and {{Sentiment Analysis}}},
  volume = {28},
  issn = {1541-1672},
  doi = {10.1109/MIS.2013.30},
  abstract = {The distillation of knowledge from the Web\—also known as opinion mining and sentiment analysis\—is a task that has recently raised growing interest for purposes such as customer service, predicting financial markets, monitoring public security, investigating elections, and measuring a health-related quality of life. This article considers past, present, and future trends of sentiment analysis by delving into the evolution of different tools and techniques\—from heuristics to discourse structure, from coarse- to fine-grained analysis, and from keyword- to concept-level opinion mining.},
  number = {2},
  journal = {IEEE Intelligent Systems},
  author = {Cambria, Erik and Schuller, Bjorn and Xia, Yunqing and Havasi, Catherine},
  month = mar,
  year = {2013},
  keywords = {Semantics,Pragmatics,Intelligent systems,Natural language processing,Context awareness,Market research,Data mining,Information analysis,Knowledge discovery,sentiment analysis,AI,NLP,opinion mining},
  pages = {15--21}
}

@article{piryani_analytical_2016,
  title = {Analytical Mapping of Opinion Mining and Sentiment Analysis Research during 2000\textendash{}2015},
  volume = {53},
  doi = {10.1016/j.ipm.2016.07.001},
  abstract = {The new transformed read-write Web has resulted in a rapid growth of user generated content on the Web resulting into a huge volume of unstructured data. A substantial part of this data is unstructured text such as reviews and blogs. Opinion mining and sentiment analysis (OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2000\textendash{}2016. For the scientometric mapping, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, types of authorship of papers on OMSA, collaboration patterns in publications on OMSA, most productive countries, institutions, journals and authors, citation patterns and an year-wise citation reference network, and theme density plots and keyword bursts in OMSA publications during the period. A somewhat detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexicon-based) used in these publications, levels (document, sentence or aspect-level) of sentiment analysis work done and major application areas of OMSA. The paper presents a detailed analytical mapping of OMSA research work and charts the progress of discipline on various useful parameters.},
  journal = {Information Processing \& Management},
  author = {Piryani, Rajesh and Madhavi, D and Singh, Vivek},
  month = jul,
  year = {2016},
  file = {/Users/Brad/Zotero/storage/9VG2NPUX/305460095_Analytical_mapping_of_opinion_mining_and_sentiment_analysis_research_during_2000-2015.html}
}

@inproceedings{pak_twitter_2010,
  title = {Twitter as a {{Corpus}} for {{Sentiment Analysis}} and {{Opinion Mining}}},
  volume = {10},
  abstract = {Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday. Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. We perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier , that is able to determine positive, negative and neutral se ntiments for a document. Experimental evaluations show that our proposed techniques are efficient and performs better than previousl y proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.},
  booktitle = {Proceedings of {{LREC}}},
  author = {Pak, Alexander and Paroubek, Patrick},
  month = jan,
  year = {2010},
  file = {/Users/Brad/Zotero/storage/MSN8HGRX/220746311_Twitter_as_a_Corpus_for_Sentiment_Analysis_and_Opinion_Mining.html}
}

@article{jansen_twitter_2009,
  title = {Twitter {{Power}}: {{Tweets As Electronic Word}} of {{Mouth}}},
  volume = {60},
  issn = {1532-2882},
  shorttitle = {Twitter {{Power}}},
  doi = {10.1002/asi.v60:11},
  abstract = {In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19\% of microblogs contain mention of a brand. Of the branding microblogs, nearly 20\% contained some expression of brand sentiments. Of these, more than 50\% were positive and 33\% were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy. \textcopyright{} 2009 Wiley Periodicals, Inc.},
  number = {11},
  journal = {J. Am. Soc. Inf. Sci. Technol.},
  author = {Jansen, Bernard J. and Zhang, Mimi and Sobel, Kate and Chowdury, Abdur},
  month = nov,
  year = {2009},
  keywords = {sentiment analysis,Twitter,classification,marketing,social networking},
  pages = {2169--2188}
}

@inproceedings{read_weakly_2009,
  address = {New York, NY, USA},
  series = {TSA '09},
  title = {Weakly {{Supervised Techniques}} for {{Domain}}-Independent {{Sentiment Classification}}},
  isbn = {978-1-60558-805-6},
  doi = {10.1145/1651461.1651470},
  abstract = {An important sub-task of sentiment analysis is polarity classification, in which text is classified as being positive or negative. Supervised machine learning techniques can perform this task very effectively. However, they require a large corpus of training data, and a number of studies have demonstrated that the good performance of supervised models is dependent on a good match between the training and testing data with respect to the domain, topic and time-period. Weakly-supervised techniques use a large collection of unlabelled text to determine sentiment, and so their performance may be less dependent on the domain, topic and time-period represented by the testing data. This paper presents experiments that investigate the effectiveness of word similarity techniques when performing weakly-supervised sentiment classification. It also considers the extent to which the performance of each method is independent from the domain, topic and time-period of the testing data. The results indicate that the word similarity techniques are suitable for applications that require sentiment classification across several domains.},
  booktitle = {Proceedings of the 1st {{International CIKM Workshop}} on {{Topic}}-Sentiment {{Analysis}} for {{Mass Opinion}}},
  publisher = {{ACM}},
  author = {Read, Jonathon and Carroll, John},
  year = {2009},
  keywords = {sentiment analysis,distributional similarity,lexical association,semantic spaces},
  pages = {45--52},
  file = {/Users/Brad/Zotero/storage/NPG6F2PF/Read and Carroll - 2009 - Weakly Supervised Techniques for Domain-independen.pdf}
}

@article{TaboadaLexiconbasedMethodsSentiment2011,
  title = {Lexicon-Based {{Methods}} for {{Sentiment Analysis}}},
  volume = {37},
  issn = {0891-2017},
  doi = {10.1162/COLI_a_00049},
  abstract = {We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.},
  number = {2},
  journal = {Comput. Linguist.},
  author = {Taboada, Maite and Brooke, Julian and Tofiloski, Milan and Voll, Kimberly and Stede, Manfred},
  month = jun,
  year = {2011},
  pages = {267--307},
  file = {/Users/Brad/Zotero/storage/UAK2FS8K/Taboada et al. - 2011 - Lexicon-based Methods for Sentiment Analysis.pdf}
}

@inproceedings{esuli_sentiwordnet:_2006,
  title = {{{SENTIWORDNET}}: {{A Publicly Available Lexical Resource}} for {{Opinion Mining}}},
  shorttitle = {{{SENTIWORDNET}}},
  abstract = {Opinion mining (OM) is a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. OM has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. In order to aid the extraction of opinions from text, recent research has tried to automatically determine the "PN-polarity" of subjective terms, i.e. identify whether a term that is a marker of opinionated content has a positive or a negative connotation. Research on determining whether a term is indeed a marker of opinionated content (a subjective term) or not (an objective term) has been, instead, much more scarce. In this work we describe SENTIWORDNET, a lexical resource in which each WORDNET synset s is associated to three numerical scores Obj(s), Pos(s) and Neg(s), describing how objective, positive, and negative the terms contained in the synset are. The method used to develop SENTIWORDNET is based on the quantitative analysis of the glosses associated to synsets, and on the use of the resulting vectorial term representations for semi-supervised synset classification. The three scores are derived by combining the results produced by a committee of eight ternary classifiers, all characterized by similar accuracy levels but different classification behaviour. SENTIWORDNET is freely available for research purposes, and is endowed with a Web-based graphical user interface.},
  booktitle = {In {{Proceedings}} of the 5th {{Conference}} on {{Language Resources}} and {{Evaluation}} ({{LREC}}'06},
  author = {Esuli, Andrea and Sebastiani, Fabrizio},
  year = {2006},
  pages = {417--422},
  file = {/Users/Brad/Zotero/storage/DU7A2RVM/Esuli and Sebastiani - 2006 - SENTIWORDNET A Publicly Available Lexical Resourc.pdf;/Users/Brad/Zotero/storage/NSEIQQUG/summary.html}
}

@article{cho_data-driven_2014,
  title = {Data-Driven Integration of Multiple Sentiment Dictionaries for Lexicon-Based Sentiment Classification of Product Reviews},
  volume = {71},
  issn = {0950-7051},
  doi = {10.1016/j.knosys.2014.06.001},
  abstract = {In lexicon-based sentiment classification, the problem of contextual polarity must be explicitly handled since it is a major cause for classification error. One way to handle contextual polarity is to revise the prior polarity of the sentiment dictionary to fit the domain. This paper presents a data-driven method of adapting sentiment dictionaries to diverse domains. Our method first merges multiple sentiment dictionaries at the entry word level to expand the dictionary. Then, leveraging the ratio of the positive/negative training data, it selectively removes the entry words that do not contribute to the classification. Finally, it selectively switches the sentiment polarity of the entry words to adapt to the domain. In essence, our method compares the positive/negative review's dictionary word occurrence ratios with the positive/negative review ratio itself to determine which entry words to be removed and which entry words' sentiment polarity to be switched. We show that the integrated sentiment dictionary constructed using our `merge', `remove', and `switch' operations robustly outperforms individual dictionaries in the sentiment classification of product reviews across different domains such as smartphones, movies, and books.},
  number = {Supplement C},
  journal = {Knowledge-Based Systems},
  author = {Cho, Heeryon and Kim, Songkuk and Lee, Jongseo and Lee, Jong-Seok},
  month = nov,
  year = {2014},
  keywords = {sentiment analysis,Sentiment dictionary,Domain adaptation,Integration,Lexicon-based review classification},
  pages = {61--71},
  file = {/Users/Brad/Zotero/storage/EVMF5CZC/Cho et al. - 2014 - Data-driven integration of multiple sentiment dict.pdf;/Users/Brad/Zotero/storage/G53ATFP2/S0950705114002196.html}
}

@inproceedings{stone_computer_1963,
  address = {New York, NY, USA},
  series = {AFIPS '63 (Spring)},
  title = {A {{Computer Approach}} to {{Content Analysis}}: {{Studies Using}} the {{General Inquirer System}}},
  shorttitle = {A {{Computer Approach}} to {{Content Analysis}}},
  doi = {10.1145/1461551.1461583},
  abstract = {The General Inquirer is an IBM 7090 program system that was developed at Harvard in the spring of 1961 for content analysis research problems in the behavioral sciences. The first part of this paper describes this system and how it has been used. During the summer of 1962, the General Inquirer was merged with the Hunt Concept Learner to produce a method for automatic theme analysis. The second part of this paper discusses the rationale behind this development and some recent signs of its future promise.},
  booktitle = {Proceedings of the {{May}} 21-23, 1963, {{Spring Joint Computer Conference}}},
  publisher = {{ACM}},
  author = {Stone, Philip J. and Hunt, Earl B.},
  year = {1963},
  pages = {241--256},
  file = {/Users/Brad/Zotero/storage/4398UHDG/Stone and Hunt - 1963 - A Computer Approach to Content Analysis Studies U.pdf}
}

@inproceedings{mohammad_generating_2009,
  address = {Stroudsburg, PA, USA},
  series = {EMNLP '09},
  title = {Generating {{High}}-Coverage {{Semantic Orientation Lexicons}} from {{Overtly Marked Words}} and a {{Thesaurus}}},
  isbn = {978-1-932432-62-6},
  abstract = {Sentiment analysis often relies on a semantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lexicons, but they tend to be computationally expensive, and usually rely on significant manual annotation and large corpora. Most of these methods use WordNet. In contrast, we propose a simple approach to generate a high-coverage semantic orientation lexicon, which includes both individual words and multi-word expressions, using only a Roget-like thesaurus and a handful of affixes. Further, the lexicon has properties that support the Polyanna Hypothesis. Using the General Inquirer as gold standard, we show that our lexicon has 14 percentage points more correct entries than the leading WordNet-based high-coverage lexicon (SentiWordNet). In an extrinsic evaluation, we obtain significantly higher performance in determining phrase polarity using our thesaurus-based lexicon than with any other. Additionally, we explore the use of visualization techniques to gain insight into the our algorithm beyond the evaluations mentioned above.},
  booktitle = {Proceedings of the 2009 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}: {{Volume}} 2 - {{Volume}} 2},
  publisher = {{Association for Computational Linguistics}},
  author = {Mohammad, Saif and Dunne, Cody and Dorr, Bonnie},
  year = {2009},
  pages = {599--608},
  file = {/Users/Brad/Zotero/storage/4X49JCH3/Mohammad et al. - 2009 - Generating High-coverage Semantic Orientation Lexi.pdf}
}

@inproceedings{canneyt_topic-dependent_2015,
  series = {Lecture Notes in Computer Science},
  title = {Topic-{{Dependent Sentiment Classification}} on {{Twitter}}},
  isbn = {978-3-319-16353-6 978-3-319-16354-3},
  doi = {10.1007/978-3-319-16354-3_48},
  abstract = {In this paper, we investigate how discovering the topic dicussed in a tweet can be used to improve its sentiment classification. In particular, a classifier is introduced consisting of a topic-specific classifier, which is only trained on tweets of the same topic of the given tweet, and a generic classifier, which is trained on all the tweets in the training set. The set of considered topics is obtained by clustering the hashtags that occur in the training set. A classifier is then used to estimate the topic of a previously unseen tweet. Experimental results based on a public Twitter dataset show that considering topic-specific sentiment classifiers indeed leads to an improvement.},
  language = {en},
  booktitle = {Advances in {{Information Retrieval}}},
  publisher = {{Springer, Cham}},
  author = {Canneyt, Steven Van and Claeys, Nathan and Dhoedt, Bart},
  month = mar,
  year = {2015},
  pages = {441--446},
  file = {/Users/Brad/Zotero/storage/IF9GISPC/Canneyt et al. - 2015 - Topic-Dependent Sentiment Classification on Twitte.pdf;/Users/Brad/Zotero/storage/HCVBMPJW/978-3-319-16354-3_48.html}
}

@article{bollen_modeling_2009,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {0911.1583},
  primaryClass = {cs},
  title = {Modeling Public Mood and Emotion: {{Twitter}} Sentiment and Socio-Economic Phenomena},
  shorttitle = {Modeling Public Mood and Emotion},
  abstract = {Microblogging is a form of online communication by which users broadcast brief text updates, also known as tweets, to the public or a selected circle of contacts. A variegated mosaic of microblogging uses has emerged since the launch of Twitter in 2006: daily chatter, conversation, information sharing, and news commentary, among others. Regardless of their content and intended use, tweets often convey pertinent information about their author's mood status. As such, tweets can be regarded as temporally-authentic microscopic instantiations of public mood state. In this article, we perform a sentiment analysis of all public tweets broadcasted by Twitter users between August 1 and December 20, 2008. For every day in the timeline, we extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) using an extended version of the Profile of Mood States (POMS), a well-established psychometric instrument. We compare our results to fluctuations recorded by stock market and crude oil price indices and major events in media and popular culture, such as the U.S. Presidential Election of November 4, 2008 and Thanksgiving Day. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.},
  journal = {arXiv:0911.1583 [cs]},
  author = {Bollen, Johan and Pepe, Alberto and Mao, Huina},
  month = nov,
  year = {2009},
  keywords = {Computer Science - Computers and Society},
  file = {/Users/Brad/Zotero/storage/5EX6PI4V/Bollen et al. - 2009 - Modeling public mood and emotion Twitter sentimen.pdf;/Users/Brad/Zotero/storage/ARZ3MMIU/0911.html}
}

@article{pepe_between_2008,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {0801.3864},
  primaryClass = {cs},
  title = {Between Conjecture and Memento: Shaping a Collective Emotional Perception of the Future},
  shorttitle = {Between Conjecture and Memento},
  abstract = {Large scale surveys of public mood are costly and often impractical to perform. However, the web is awash with material indicative of public mood such as blogs, emails, and web queries. Inexpensive content analysis on such extensive corpora can be used to assess public mood fluctuations. The work presented here is concerned with the analysis of the public mood towards the future. Using an extension of the Profile of Mood States questionnaire, we have extracted mood indicators from 10,741 emails submitted in 2006 to futureme.org, a web service that allows its users to send themselves emails to be delivered at a later date. Our results indicate long-term optimism toward the future, but medium-term apprehension and confusion.},
  journal = {arXiv:0801.3864 [cs]},
  author = {Pepe, Alberto and Bollen, Johan},
  month = jan,
  year = {2008},
  keywords = {Computer Science - Computation and Language,Computer Science - General Literature},
  file = {/Users/Brad/Zotero/storage/XSZ9A4IW/Pepe and Bollen - 2008 - Between conjecture and memento shaping a collecti.pdf;/Users/Brad/Zotero/storage/U59U7HFB/0801.html}
}

@misc{_evaluation_????,
  title = {Evaluation Datasets for {{Twitter}} Sentiment Analysis: A Survey and a New Dataset, the {{STS}}-{{Gold}} - {{Open Research Online}}},
  howpublished = {http://oro.open.ac.uk/40660/}
}

@article{kharde_sentiment_2016,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1601.06971},
  title = {Sentiment {{Analysis}} of {{Twitter Data}}: {{A Survey}} of {{Techniques}}},
  volume = {139},
  issn = {09758887},
  shorttitle = {Sentiment {{Analysis}} of {{Twitter Data}}},
  doi = {10.5120/ijca2016908625},
  abstract = {With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper.},
  number = {11},
  journal = {International Journal of Computer Applications},
  author = {Kharde, Vishal A. and Sonawane, Prof Sheetal},
  month = apr,
  year = {2016},
  keywords = {Computer Science - Computation and Language,I.2.7},
  pages = {5--15},
  file = {/Users/Brad/Zotero/storage/U6H9Z42K/Kharde and Sonawane - 2016 - Sentiment Analysis of Twitter Data A Survey of Te.pdf;/Users/Brad/Zotero/storage/QBQV97RC/1601.html}
}

@inproceedings{wu_exploration_2011,
  title = {An {{Exploration}} of {{Social Media}} in {{Public Opinion Convergence}}: {{Elaboration Likelihood}} and {{Semantic Networks}} on {{Political Events}}},
  shorttitle = {An {{Exploration}} of {{Social Media}} in {{Public Opinion Convergence}}},
  doi = {10.1109/DASC.2011.151},
  abstract = {This study investigated use of social media to enable public opinion convergence in activities with wide-scale interaction, such as in political events. Using theoretical foundations in elaboration likelihood, we explored the process of opinion convergence by analyzing Twitter data of Singapore General Election 2011. Our quantitative analyses showed that informative tweets were more effective than affective tweets in opinion convergence, and their interactive effect on social impact was significant. In addition, qualitative analyses using semantic network maps enabled us to distinguish the central and peripheral routes of influence. We discussed both theoretical and practical implications of our findings with respect to social impact and new media.},
  booktitle = {2011 {{IEEE Ninth International Conference}} on {{Dependable}}, {{Autonomic}} and {{Secure Computing}}},
  author = {Wu, Y. and Wong, J. and Deng, Y. and Chang, K.},
  month = dec,
  year = {2011},
  keywords = {Semantics,Twitter,politics,semantic networks,social networking (online),Singapore General Election 2011,Twitter data,elaboration likelihood,political events,public opinion convergence,social media,Convergence,Frequency measurement,Information processing,Media,Nominations and elections,affective tweet,elaboration likelihood model,informative tweet,opinion convergence},
  pages = {903--910},
  file = {/Users/Brad/Zotero/storage/JQ4NX5ZB/6118885.html}
}

@article{jonathon_using_2005,
  title = {Using {{Emoticons}} to Reduce {{Dependency}} in {{Machine Learning Techniques}} for {{Sentiment Classification}}},
  abstract = {Sentiment Classification seeks to identify a piece of text according to its author{\"\i}\textquestiondown\textonehalf{}s general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and \ldots{}},
  journal = {Microsoft Research},
  author = {Jonathon and Read},
  month = jan,
  year = {2005},
  file = {/Users/Brad/Zotero/storage/8H3V2M86/using-emoticons-to-reduce-dependency-in-machine-learning-techniques-for-sentiment-classificatio.html}
}

@article{lawlor_smart_2012,
  title = {Smart {{Goals}}: {{How}} the {{Application}} of {{Smart Goals}} Can {{Contribute}} to {{Achievement}} of {{Student Learning Outcomes}}},
  volume = {39},
  copyright = {Copyright (c) 2017 Developments in Business Simulation and Experiential Learning},
  shorttitle = {Smart {{Goals}}},
  abstract = {This paper provides a discussion of SMART goals and how the utilization of a SMART goals spreadsheet within the classroom can enhance student learning outcomes. The authors conducted a comparison of Management Fundamental classes from 2010 to 2011 on a major project required for the class to determine if students can improve their performances by requiring them to utilize SMART goals early in the semester. Early results indicate that this proposal may be true. This paper first reviews the history of SMART goals and then provides an example on how teachers can utilize the tool within the classroom setting to improve student learning outcomes.},
  language = {en},
  number = {0},
  journal = {Developments in Business Simulation and Experiential Learning},
  author = {Lawlor, K. Blaine},
  year = {2012},
  file = {/Users/Brad/Zotero/storage/W2R49J6Z/Lawlor - 2012 - Smart Goals How the Application of Smart Goals ca.pdf;/Users/Brad/Zotero/storage/WIICK4IF/90.html}
}

@article{doran_theres_1981,
  title = {There's a {{S}}.{{M}}.{{A}}.{{R}}.{{T}}. Way to Write Managements's Goals and Objectives.},
  volume = {70},
  issn = {0025-1895},
  number = {11},
  journal = {Management Review},
  author = {Doran, George T.},
  year = {1981},
  pages = {35--36},
  file = {/Users/Brad/Zotero/storage/W4ENDJQ6/F0FCAE1C-F945-430E-9185-7F07DAA01AD0.html}
}

@inproceedings{tumasjan_predicting_2010,
  title = {Predicting {{Elections}} with {{Twitter}}: {{What}} 140 {{Characters Reveal}} about {{Political Sentiment}}},
  copyright = {Authors who publish a paper in this conference agree to the following terms:    1. Author(s) agree to transfer their copyrights in their article/paper to the Association for the Advancement of Artificial Intelligence (AAAI), in order to deal with future requests for reprints, translations, anthologies, reproductions, excerpts, and other publications. This grant will include, without limitation, the entire copyright in the article/paper in all countries of the world, including all renewals, extensions, and reversions thereof, whether such rights current exist or hereafter come into effect, and also the exclusive right to create electronic versions of the article/paper, to the extent that such right is not subsumed under copyright.    2. The author(s) warrants that they are the sole author and owner of the copyright in the above article/paper, except for those portions shown to be in quotations; that the article/paper is original throughout; and that the undersigned right to make the grants set forth above is complete and unencumbered.    3. The author(s) agree that if anyone brings any claim or action alleging facts that, if true, constitute a breach of any of the foregoing warranties, the author(s) will hold harmless and indemnify AAAI, their grantees, their licensees, and their distributors against any liability, whether under judgment, decree, or compromise, and any legal fees and expenses arising out of that claim or actions, and the undersigned will cooperate fully in any defense AAAI may make to such claim or action. Moreover, the undersigned agrees to cooperate in any claim or other action seeking to protect or enforce any right the undersigned has granted to AAAI in the article/paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, the undersigned agrees to reimburse whomever brings such claim or action for expenses and attorneys' fees incurred therein.    4. Author(s) retain all proprietary rights other than copyright (such as patent rights).    5. Author(s) may make personal reuse of all or portions of the above article/paper in other works of their own authorship.    6. Author(s) may reproduce, or have reproduced, their article/paper for the author's personal use, or for company use provided that AAAI copyright and the source are indicated, and that the copies are not used in a way that implies AAAI endorsement of a product or service of an employer, and that the copies per se are not offered for sale. The foregoing right shall not permit the posting of the article/paper in electronic or digital form on any computer network, except by the author or the author's employer, and then only on the author's or the employer's own web page or ftp site. Such web page or ftp site, in addition to the aforementioned requirements of this Paragraph, must provide an electronic reference or link back to the AAAI electronic server, and shall not post other AAAI copyrighted materials not of the author's or the employer's creation (including tables of contents with links to other papers) without AAAI's written permission.    7. Author(s) may make limited distribution of all or portions of their article/paper prior to publication.    8. In the case of work performed under U.S. Government contract, AAAI grants the U.S. Government royalty-free permission to reproduce all or portions of the above article/paper, and to authorize others to do so, for U.S. Government purposes.    9. In the event the above article/paper is not accepted and published by AAAI, or is withdrawn by the author(s) before acceptance by AAAI, this agreement becomes null and void.},
  shorttitle = {Predicting {{Elections}} with {{Twitter}}},
  abstract = {Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets\’ political sentiment demonstrates close correspondence to the parties' and politicians\’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.},
  language = {en},
  booktitle = {Fourth {{International AAAI Conference}} on {{Weblogs}} and {{Social Media}}},
  author = {Tumasjan, Andranik and Sprenger, Timm O. and Sandner, Philipp G. and Welpe, Isabell M.},
  month = may,
  year = {2010},
  file = {/Users/Brad/Zotero/storage/6A8IZAM2/Tumasjan et al. - 2010 - Predicting Elections with Twitter What 140 Charac.pdf;/Users/Brad/Zotero/storage/PBUMQS7Q/1441.html}
}

@article{anders_olof_larsson_studying_2012,
  title = {Studying Political Microblogging: {{Twitter}} Users in the 2010 {{Swedish}} Election Campaign},
  volume = {14},
  issn = {1461-4448},
  shorttitle = {Studying Political Microblogging},
  doi = {10.1177/1461444811422894},
  abstract = {Among the many so-called microblogging services that allow their users to describe their current status in short posts, Twitter is probably among the most popular and well known. Since its launch in 2006, Twitter use has evolved and is increasingly used in a variety of contexts. This article utilizes emerging online tools and presents a rationale for data collection and analysis of Twitter users. The suggested approach is exemplified with a case study: Twitter use during the 2010 Swedish election. Although many of the initial hopes for e-democracy appear to have gone largely unfulfilled, the successful employment of the internet during the 2008 US presidential campaign has again raised voices claiming that the internet, and particularly social media applications like Twitter, provides interesting opportunities for online campaigning and deliberation. Besides providing an overarching analysis of how Twitter use was fashioned during the 2010 Swedish election campaign, this study identifies different user types based on how high-end users utilized the Twitter service. By suggesting a novel approach to the study of microblogging and by identifying user types, this study contributes to the burgeoning field of microblog research and gives specific insights into the practice of civic microblogging.},
  language = {en},
  number = {5},
  journal = {New Media \& Society},
  author = {{Anders Olof Larsson} and {Hallvard Moe}},
  month = aug,
  year = {2012},
  pages = {729--747},
  file = {/Users/Brad/Zotero/storage/KHHTB9P7/Anders Olof Larsson and Hallvard Moe - 2012 - Studying political microblogging Twitter users in.pdf}
}

@article{jensen_psephological_2013,
  title = {Psephological Investigations: {{Tweets}}, Votes, and Unknown Unknowns in the Republican Nomination Process},
  volume = {5},
  issn = {1944-2866},
  shorttitle = {Psephological Investigations},
  doi = {10.1002/1944-2866.POI329},
  language = {en},
  number = {2},
  journal = {Policy \& Internet},
  author = {Jensen, Michael J. and Anstead, Nick},
  month = jun,
  year = {2013},
  pages = {161--182},
  file = {/Users/Brad/Zotero/storage/S845HGJG/Jensen and Anstead - 2013 - Psephological investigations Tweets, votes, and u.pdf;/Users/Brad/Zotero/storage/98TVJXKV/full.html}
}

@article{andreas_jungherr_why_2012,
  title = {Why the {{Pirate Party Won}} the {{German Election}} of 2009 or {{The Trouble With Predictions}}: {{A Response}} to {{Tumasjan}}, {{A}}., {{Sprenger}}, {{T}}. {{O}}., {{Sander}}, {{P}}. {{G}}., \& {{Welpe}}, {{I}}. {{M}}. ``{{Predicting Elections With Twitter}}: {{What}} 140 {{Characters Reveal About Political Sentiment}}''},
  volume = {30},
  issn = {0894-4393},
  shorttitle = {Why the {{Pirate Party Won}} the {{German Election}} of 2009 or {{The Trouble With Predictions}}},
  doi = {10.1177/0894439311404119},
  abstract = {In their article ``Predicting Elections with Twitter: What 140 Characters Reveal About Political Sentiment,'' the authors Andranik Tumasjan, Timm O. Sprenger, Philipp G. Sandner, and Isabell M. Welpe (TSSW) the authors claim that it would be possible to predict election outcomes in Germany by examining the relative frequency of the mentions of political parties in Twitter messages posted during the election campaign. In this response we show that the results of TSSW are contingent on arbitrary choices of the authors. We demonstrate that as of yet the relative frequency of mentions of German political parties in Twitter message allows no prediction of election results.},
  language = {en},
  number = {2},
  journal = {Social Science Computer Review},
  author = {{Andreas Jungherr} and {Pascal J{\"u}rgens} and {Harald Schoen}},
  month = may,
  year = {2012},
  pages = {229--234},
  file = {/Users/Brad/Zotero/storage/S2CGC32E/Andreas Jungherr et al. - 2012 - Why the Pirate Party Won the German Election of 20.pdf}
}

@inproceedings{wang_system_2012,
  address = {Stroudsburg, PA, USA},
  series = {ACL '12},
  title = {A {{System}} for {{Real}}-Time {{Twitter Sentiment Analysis}} of 2012 {{U}}.{{S}}. {{Presidential Election Cycle}}},
  abstract = {This paper describes a system for real-time analysis of public sentiment toward presidential candidates in the 2012 U.S. election as expressed on Twitter, a micro-blogging service. Twitter has become a central site where people express their opinions and views on political parties and candidates. Emerging events or news are often followed almost instantly by a burst in Twitter volume, providing a unique opportunity to gauge the relation between expressed public sentiment and electoral events. In addition, sentiment analysis can help explore how these events affect public opinion. While traditional content analysis takes days or weeks to complete, the system demonstrated here analyzes sentiment in the entire Twitter traffic about the election, delivering results instantly and continuously. It offers the public, the media, politicians and scholars a new and timely perspective on the dynamics of the electoral process and public opinion.},
  booktitle = {Proceedings of the {{ACL}} 2012 {{System Demonstrations}}},
  publisher = {{Association for Computational Linguistics}},
  author = {Wang, Hao and Can, Dogan and Kazemzadeh, Abe and Bar, Fran{\c c}ois and Narayanan, Shrikanth},
  year = {2012},
  pages = {115--120},
  file = {/Users/Brad/Zotero/storage/BZHEEAJ3/Wang et al. - 2012 - A System for Real-time Twitter Sentiment Analysis .pdf}
}

@inproceedings{pennacchiotti_machine_2011,
  title = {A {{Machine Learning Approach}} to {{Twitter User Classification}}},
  volume = {11},
  booktitle = {{{ICWSM}}},
  author = {Pennacchiotti, Marco and Popescu, Ana-Maria},
  month = jan,
  year = {2011}
}

@misc{porcaro_tweeting_2016,
  title = {Tweeting {{Brexit}}: {{Narrative}} Building and Sentiment Analysis | {{Bruegel}}},
  shorttitle = {Tweeting {{Brexit}}},
  abstract = {Public discourse on social media was already in favour of Brexit by early summer 2015, and stayed that way until the referendum. An analysis of more than 890 000 tweets posted since 2012 reveals clear trends in the mood of online discussion. Our new methodology captures something that betting odds and opinion polls were not able to reveal \textendash{} but will it be useful in future elections?},
  journal = {Tweeting Brexit: Narrative building and sentiment analysis},
  author = {Porcaro, Giuseppe and M{\"u}ller, Henrik},
  month = nov,
  year = {2016},
  file = {/Users/Brad/Zotero/storage/VPW2GB5P/tweeting-brexit-narrative-building-and-sentiment-analysis.html}
}

@misc{matsuo_more_2017,
  title = {More Positive, Assertive and Forward-Looking: How {{Leave}} Won {{Twitter}}},
  shorttitle = {More Positive, Assertive and Forward-Looking},
  abstract = {How did people talk about the EU referendum on Twitter?~Akitaka Matsuo and Kenneth Benoit (left) analysed 23m tweets about Brexit, and found salient differences between Leave and Remain supporters.\ldots{}},
  journal = {LSE BREXIT},
  author = {Matsuo, Akitaka and Benoit, Kenneth},
  month = mar,
  year = {2017},
  file = {/Users/Brad/Zotero/storage/4GHSQBCW/more-positive-assertive-and-forward-looking-how-leave-won-twitter.html}
}

@article{maynard_framework_2017,
  series = {Industry and In-use Applications of Semantic Technologies},
  title = {A Framework for Real-Time Semantic Social Media Analysis},
  volume = {44},
  issn = {1570-8268},
  doi = {10.1016/j.websem.2017.05.002},
  abstract = {This paper presents a framework for collecting and analysing large volume social media content. The real-time analytics framework comprises semantic annotation, Linked Open Data, semantic search, and dynamic result aggregation components. In addition, exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices, term clouds, treemaps, and choropleths. There is also an interactive semantic search interface (Prospector), where users can save, refine, and analyse the results of semantic search queries over time. Practical use of the framework is exemplified through three case studies: a general scenario analysing tweets from UK politicians and the public's response to them in the run up to the 2015 UK general election, an investigation of attitudes towards climate change expressed by these politicians and the public, via their engagement with environmental topics, and an analysis of public tweets leading up to the UK's referendum on leaving the EU (Brexit) in 2016. The paper also presents a brief evaluation and discussion of some of the key text analysis components, which are specifically adapted to the domain and task, and demonstrate scalability and efficiency of our toolkit in the case studies.},
  number = {Supplement C},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  author = {Maynard, Diana and Roberts, Ian and Greenwood, Mark A. and Rout, Dominic and Bontcheva, Kalina},
  month = may,
  year = {2017},
  keywords = {Natural language processing,sentiment analysis,Semantic search,Social media analysis,Linked Open Data,Semantic annotation},
  pages = {75--88},
  file = {/Users/Brad/Zotero/storage/Q6UR3DKC/Maynard et al. - 2017 - A framework for real-time semantic social media an.pdf;/Users/Brad/Zotero/storage/AM3E9C2F/S1570826817300240.html}
}

@article{llewellyn_brexit?_2016,
  title = {Brexit? {{Analyzing Opinion}} on the {{UK}}-{{EU Referendum}} within {{Twitter}}},
  shorttitle = {Brexit?},
  abstract = {Description},
  language = {English},
  author = {Llewellyn, Clare and Cram, Laura},
  month = mar,
  year = {2016},
  file = {/Users/Brad/Zotero/storage/G49USV6D/export.html}
}

@book{VolkovaExploringSentimentSocial,
  title = {Exploring {{Sentiment}} in {{Social Media}}: {{Bootstrapping Subjectivity Clues}} from {{Multilingual Twitter Streams}}},
  shorttitle = {Exploring {{Sentiment}} in {{Social Media}}},
  abstract = {We study subjective language in social media and create Twitter-specific lexicons via bootstrapping sentiment-bearing terms from multilingual Twitter streams. Starting with a domain-independent, highprecision sentiment lexicon and a large pool of unlabeled data, we bootstrap Twitter-specific sentiment lexicons, using a small amount of labeled data to guide the process. Our experiments on English, Spanish and Russian show that the resulting lexicons are effective for sentiment classification for many underexplored languages in social media. 1},
  author = {Volkova, Svitlana and Wilson, Theresa and Yarowsky, David},
  file = {/Users/Brad/Zotero/storage/XAG2RL27/Volkova et al. - Exploring Sentiment in Social Media Bootstrapping.pdf;/Users/Brad/Zotero/storage/5BFGMGIH/summary.html}
}

@misc{OneSeedFind,
  title = {One {{Seed}} to {{Find Them All}}: {{Mining Opinion Features}} via {{Association}} - {{Google Search}}},
  howpublished = {https://www.google.co.uk/search?q=One+Seed+to+Find+Them+All\%3A+Mining+Opinion+Features+via+Association\&oq=One+Seed+to+Find+Them+All\%3A+Mining+Opinion+Features+via+Association\&aqs=chrome..69i57j69i61.235j0j7\&sourceid=chrome\&ie=UTF-8},
  file = {/Users/Brad/Zotero/storage/JWXEJZPT/search.html}
}

@inproceedings{HaiOneseedfind2012,
  title = {One Seed to Find Them All : Mining Opinion Features via Association},
  shorttitle = {One Seed to Find Them All},
  doi = {http://dx.doi.org/10.1145/2396761.2396797},
  abstract = {Feature-based opinion analysis has attracted extensive attention recently. Identifying features associated with opinions expressed in reviews is essential for fine-grained opinion mining. One approach is to exploit the dependency relations that occur naturally between features and opinion words, and among features (or opinion words) themselves. In this paper, we propose a generalized approach to opinion feature extraction by incorporating robust statistical association analysis in a bootstrapping framework. The new approach starts with a small set of feature seeds, on which it iteratively enlarges by mining feature-opinion, feature-feature, and opinion-opinion dependency relations. Two association model types, namely likelihood ratio tests (LRT) and latent semantic analysis (LSA), are proposed for computing the pair-wise associations between terms (features or opinions). We accordingly propose two robust bootstrapping approaches, LRTBOOT and LSABOOT, both of which need just a handful of initial feature seeds to bootstrap opinion feature extraction. We benchmarked LRTBOOT and LSABOOT against existing approaches on a large number of real-life reviews crawled from the cellphone and hotel domains. Experimental results using varying number of feature seeds show that the proposed association-based bootstrapping approach significantly outperforms the competitors. In fact, one seed feature is all that is needed for LRTBOOT to significantly outperform the other methods. This seed feature can simply be the domain feature, e.g., "cellphone" or "hotel". The consequence of our discovery is far reaching: starting with just one feature seed, typically just the domain concept word, LRTBOOT can automatically extract a large set of high-quality opinion features from the corpus without any supervision or labeled features. This means that the automatic creation of a set of domain features is no longer a pipe dream!},
  language = {en},
  author = {Hai, Zhen and Chang, Kuiyu and Cong, Gao},
  year = {2012},
  file = {/Users/Brad/Zotero/storage/BVE42ZE7/12292.html}
}

@misc{InstallingPythonModules,
  title = {Installing {{Python Modules}} \textemdash{} {{Python}} 3.6.5rc1 Documentation},
  howpublished = {https://docs.python.org/3/installing/index.html},
  file = {/Users/Brad/Zotero/storage/DV8ZAKIA/index.html}
}

@article{RovceMANAGINGDEVELOPMENTLARGE,
  title = {{{MANAGING THE DEVELOPMENT OF LARGE SOFTWARE SYSTEMS}}},
  language = {en},
  author = {Rovce, Dr Winston W},
  pages = {11},
  file = {/Users/Brad/Zotero/storage/YUKPCI4G/Rovce - MANAGING THE DEVELOPMENT OF LARGE SOFTWARE SYSTEMS.pdf}
}

@inproceedings{RoyceManagingDevelopmentLarge1987,
  address = {Los Alamitos, CA, USA},
  series = {ICSE '87},
  title = {Managing the {{Development}} of {{Large Software Systems}}: {{Concepts}} and {{Techniques}}},
  isbn = {978-0-89791-216-7},
  shorttitle = {Managing the {{Development}} of {{Large Software Systems}}},
  booktitle = {Proceedings of the 9th {{International Conference}} on {{Software Engineering}}},
  publisher = {{IEEE Computer Society Press}},
  author = {Royce, W. W.},
  year = {1987},
  pages = {328--338}
}

@book{CoddRecentInvestigationsRelational1974,
  title = {Recent {{Investigations}} in {{Relational Data Base Systems}}.},
  publisher = {{IBM Thomas J. Watson Research Division}},
  author = {Codd, Edgar F.},
  year = {1974}
}

@misc{CadwalladrgreatBritishBrexit2017,
  title = {The Great {{British Brexit}} Robbery: How Our Democracy Was Hijacked},
  shorttitle = {The Great {{British Brexit}} Robbery},
  abstract = {A shadowy operation involving big data, billionaire friends of Trump and the disparate forces of the Leave campaign heavily influenced the result of the EU referendum. Is our electoral process still fit for purpose?},
  language = {en-GB},
  howpublished = {http://www.theguardian.com/technology/2017/may/07/the-great-british-brexit-robbery-hijacked-democracy},
  journal = {The Guardian},
  author = {Cadwalladr, Carole},
  month = may,
  year = {2017},
  keywords = {Article 50,Big data,Brexit,Data protection,European Union,Facebook,Foreign policy,Politics,Technology,UK news},
  file = {/Users/Brad/Zotero/storage/NZCDA3NG/the-great-british-brexit-robbery-hijacked-democracy.html}
}

@misc{CadwalladrRevealedtiesthat2018,
  title = {Revealed: The Ties That Bind {{Vote Leave}}'s Data Firm to Controversial {{Cambridge Analytica}}},
  shorttitle = {Revealed},
  abstract = {Role of remote data affiliate raises questions over relationship between Brexit groups},
  language = {en},
  howpublished = {http://www.theguardian.com/uk-news/2018/mar/24/aggregateiq-data-firm-link-raises-leave-group-questions},
  journal = {the Guardian},
  author = {Cadwalladr, Carole and Townsend, Mark},
  month = mar,
  year = {2018},
  file = {/Users/Brad/Zotero/storage/WYK5B3FE/aggregateiq-data-firm-link-raises-leave-group-questions.html}
}

@misc{Tweepy,
  title = {Tweepy},
  howpublished = {http://www.tweepy.org/},
  file = {/Users/Brad/Zotero/storage/4CHUPF9X/www.tweepy.org.html}
}

@misc{RoessleinTweepyDocumentationtweepy2009,
  title = {Tweepy {{Documentation}} \textemdash{} Tweepy 3.5.0 Documentation},
  shorttitle = {Tweepy {{Documentation}}},
  howpublished = {http://tweepy.readthedocs.io/en/v3.5.0/},
  author = {Roesslein, Joshua},
  year = {2009},
  file = {/Users/Brad/Zotero/storage/MQ68AWVQ/v3.5.html}
}

@article{ChungCanCollectiveSentiment,
  title = {Can {{Collective Sentiment Expressed}} on {{Twitter Predict Political Elections}}?},
  abstract = {Research examining the predictive power of social media (especially Twitter) displays conflicting results, particularly in the domain of political elections. This paper applies methods used in studies that have shown a direct correlation between volume/sentiment of Twitter chatter and future electoral results in a new dataset about political elections. We show that these methods display a series of shortcomings, that make them inadequate for determining whether social media messages can predict the outcome of elections.},
  language = {en},
  author = {Chung, Jessica and Mustafaraj, Eni},
  pages = {2},
  file = {/Users/Brad/Zotero/storage/ISWKTATX/Chung and Mustafaraj - Can Collective Sentiment Expressed on Twitter Pred.pdf}
}

@article{MucciniSoftwarearchitecturebasedregression2006,
  title = {Software Architecture-Based Regression Testing},
  volume = {79},
  issn = {01641212},
  doi = {10.1016/j.jss.2006.02.059},
  language = {en},
  number = {10},
  journal = {Journal of Systems and Software},
  author = {Muccini, Henry and Dias, Marcio and Richardson, Debra J.},
  month = oct,
  year = {2006},
  pages = {1379--1396},
  file = {/Users/Brad/Zotero/storage/PMMMTJYU/Muccini et al. - 2006 - Software architecture-based regression testing.pdf}
}

@misc{WittgensteinTractatusLogicoPhilosophicus1922,
  title = {Tractatus {{Logico}}-{{Philosophicus}}},
  howpublished = {http://www.worldcat.org/title/tractatus-logico-philosophicus/oclc/51766172?page=citation},
  author = {Wittgenstein, Ludwig},
  year = {1922},
  file = {/Users/Brad/Zotero/storage/AL3R598P/51766172.html}
}


text:
@article
{
    ref:plotly1,
    title={plotly: Create Interactive Web Graphics via ‘plotly. js’},
    author={Sievert, Carson and Parmer, Chris and Hocking, Toby and Chamberlain, Scott and Ram, Karthik and Corvellec, Marianne and Despouy, Pedro},
    journal={R package version},
    volume={4},
    number={1},
    pages={110},
    year={2017}
}

@article
{
    ref:ada2,
    title={Multi-class adaboost},
    author={Hastie, Trevor and Rosset, Saharon and Zhu, Ji and Zou, Hui},
    journal={Statistics and its Interface},
    volume={2},
    number={3},
    pages={349--360},
    year={2009},
    publisher={International Press of Boston}
}

@article
{
    ref:ada1,
    title={A decision-theoretic generalization of on-line learning and an application to boosting},
    author={Freund, Yoav and Schapire, Robert E},
    journal={Journal of computer and system sciences},
    volume={55},
    number={1},
    pages={119--139},
    year={1997},
    publisher={Elsevier}
}

@article
{
    ref:decisiontree3,
    title={Random forests},
    author={Breiman, Leo},
    journal={Machine learning},
    volume={45},
    number={1},
    pages={5--32},
    year={2001},
    publisher={Springer}
}

@book
{
    ref:decisiontree2,
    title={The elements of statistical learning: data mining, inference, and prediction},
    author={Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
    year={2009},
    publisher={Springer Science \& Business Media}
}

@book
{
    ref:decisiontree1,
    title={Classification and regression trees},
    author={Breiman, Leo},
    year={2017},
    publisher={Routledge}
}

@article
{
    ref:svc2,
    title={Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods},
    author={Platt, John and others},
    journal={Advances in large margin classifiers},
    volume={10},
    number={3},
    pages={61--74},
    year={1999},
    publisher={Cambridge, MA}
}

@article
{
    ref:svc1,
    title={LIBSVM: A library for support vector machines},
    author={Chang, Chih-Chung and Lin, Chih-Jen},
    journal={ACM transactions on intelligent systems and technology (TIST)},
    volume={2},
    number={3},
    pages={27},
    year={2011},
    publisher={Acm}
}

@misc
{
    ref:pil1,
    title={Pillow (PIL Fork) Documentation},
    author={Clark, Alex},
    year={2015},
    publisher={Release}
}

@inproceedings
{
    ref:normal4,
    title={LOF: identifying density-based local outliers},
    author={Breunig, Markus M and Kriegel, Hans-Peter and Ng, Raymond T and Sander, J{\"o}rg},
    booktitle={ACM sigmod record},
    volume={29},
    number={2},
    pages={93--104},
    year={2000},
    organization={ACM}
}

@article
{
    ref:normal3,
    title={Isolation-based anomaly detection},
    author={Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
    journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
    volume={6},
    number={1},
    pages={3},
    year={2012},
    publisher={Acm}
}

@inproceedings
{
    ref:normal2,
    title={Isolation forest},
    author={Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
    booktitle={2008 Eighth IEEE International Conference on Data Mining},
    pages={413--422},
    year={2008},
    organization={IEEE}
}

@article
{
    ref:normal1,
    title={A fast algorithm for the minimum covariance determinant estimator},
    author={Rousseeuw, Peter J and Driessen, Katrien Van},
    journal={Technometrics},
    volume={41},
    number={3},
    pages={212--223},
    year={1999},
    publisher={Taylor \& Francis Group}
}

@article
{
    ref:tsne1,
    title={Visualizing data using t-SNE},
    author={Maaten, Laurens van der and Hinton, Geoffrey},
    journal={Journal of machine learning research},
    volume={9},
    number={Nov},
    pages={2579--2605},
    year={2008}
}

@article
{
    ref:scipy1,
    title={SciPy: Open source scientific tools for Python},
    author={Jones, Eric and Oliphant, Travis and Peterson, Pearu and others},
    year={2001},
    publisher={https://www. scipy. org}
}

@article
{
    ref:pandas1,
    title={pandas: a foundational Python library for data analysis and statistics},
    author={McKinney, Wes},
    journal={Python for High Performance and Scientific Computing},
    volume={14},
    year={2011}
}

@article
{
    ref:matplotlib1,
    title={Matplotlib: A 2D graphics environment},
    author={Hunter, John D},
    journal={Computing in science \& engineering},
    volume={9},
    number={3},
    pages={90},
    year={2007},
    publisher={IEEE Computer Society}
}

@article
{
    ref:sklearn1,
    title={Scikit-learn: Machine learning in Python},
    author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
    journal={Journal of machine learning research},
    volume={12},
    number={Oct},
    pages={2825--2830},
    year={2011}
}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
DESCRIPTION = "Machine vision demo descriptions for Matrix v2"

require recipes-core/matrix/matrix-gui-apps-git.inc

PR = "${INC_PR}.4"

PACKAGE_ARCH = "${MACHINE_ARCH}"

S = "${WORKDIR}/git/machinevision_apps"

# Make sure machinevision submenu and app images has been installed
MACHINEVISION_RDEPENDS += "matrix-gui-apps-images matrix-gui-submenus-machinevision"

BARCODE_RDEPENDS = ""
#BARCODE_RDEPENDS = " \
#    ${@['','barcode-roi'][oe.utils.all_distro_features(d, 'opencv', True, False) and bb.utils.contains('MACHINE_FEATURES', 'dsp', True, False, d)]} \
#"

FILES_${PN} += "${MATRIX_BASE_DIR}/*"

PACKAGES = "matrix-machinevision-demo-dlp3dscanner \
            matrix-machinevision-demo-barcoderoi \
            matrix-machinevision-demo-barcoderoi-f2f \
            matrix-machinevision-demo-simplepeopletracking \
"

RDEPENDS_matrix-machinevision-demo-dlp3dscanner = " \
    ${MACHINEVISION_RDEPENDS} \
"

RDEPENDS_matrix-machinevision-demo-dlp3dscanner_append_dra7xx = " \
    ${@['','point-cloud-viewer'][oe.utils.all_distro_features(d, 'opencv', True, False) and bb.utils.contains('MACHINE_FEATURES', 'gpu', True, False, d)]} \
"

RDEPENDS_matrix-machinevision-demo-barcoderoi = " \
    ${MACHINEVISION_RDEPENDS} \
    ${BARCODE_RDEPENDS} \
"

RDEPENDS_matrix-machinevision-demo-barcoderoi-f2f = " \
    ${MACHINEVISION_RDEPENDS} \
    ${BARCODE_RDEPENDS} \
"

RDEPENDS_matrix-machinevision-demo-simplepeopletracking = " \
    ${MACHINEVISION_RDEPENDS} \
"

FILES_matrix-machinevision-demo-dlp3dscanner    = "${MATRIX_APP_DIR}/machinevision_dlp_3d_scanner/*"
FILES_matrix-machinevision-demo-dlp3dscanner   += "${bindir}/runDlp3DScanner.sh"

FILES_matrix-machinevision-demo-barcoderoi    = "${MATRIX_APP_DIR}/machinevision_barcode_roi/*"
FILES_matrix-machinevision-demo-barcoderoi   += "${bindir}/runBarcodeRoi.sh"
FILES_matrix-machinevision-demo-barcoderoi-f2f = "${MATRIX_APP_DIR}/machinevision_barcode_roi_f2f/*"
FILES_matrix-machinevision-demo-barcoderoi-f2f += "${bindir}/runBarcodeRoiSave2File.sh"

FILES_matrix-machinevision-demo-simplepeopletracking   = "${MATRIX_APP_DIR}/machinevision_simple_people_tracking/*"
FILES_matrix-machinevision-demo-simplepeopletracking   += "${bindir}/runSimplePeopleTracking.sh"

extension: bb


text:
package main

import (
	"context"
	"fmt"
	"log"
	"os"
	"regexp"
	"sync"
	"time"

	se "github.com/Fedor-Bystrov/tt-sync/syncentity"
	tc "github.com/Fedor-Bystrov/tt-sync/todoistclient"
	"github.com/mongodb/mongo-go-driver/mongo"
	"github.com/mongodb/mongo-go-driver/mongo/readpref"
)

var (
	todoistToken   = os.Getenv("TODOIST_TOKEN")
	goEnv          = os.Getenv("GO_ENV")
	syncPattern, _ = regexp.Compile(`^\[SYNC\]\s.+$`)
	todoistClient  = tc.NewClient(todoistToken, nil)

	ctx, cancelFunc = context.WithTimeout(context.Background(), 10*time.Second)
	client          mongo.Client
	logFile         *os.File
)

func init() {
	defer elapsed("[Elapsed] INIT")()
	checkVars(todoistToken)

	log.Print("[Main#init] Connecting to mongo")
	client, err := mongo.Connect(ctx, "mongodb://localhost:27017")

	// Set up logging
	if goEnv != "development" {
		logFile, err := os.OpenFile("todoist-resolver.log", os.O_RDWR|os.O_CREATE|os.O_APPEND, 0666)
		if err != nil {
			log.Fatalf("Fatal [Main#init] Error opening file: %v", err)
		}
		log.SetOutput(logFile)
	}

	// Check that connection established
	err = client.Ping(ctx, readpref.Primary())
	if err != nil {
		log.Fatalf("Fatal [Main#init] Error connecting to mongo: %v", err)
	}
	log.Print("[Main#init] Connection established")
}

func main() {
	defer elapsed("[Elapsed] MAIN")()
	if goEnv != "development" {
		defer logFile.Close()
	}

	projectRespCh := make(chan []tc.Project)
	tasksRespCh := make(chan []tc.Task)
	tasksCh := make(chan se.Task)

	// 1. Fetching all projects and tasks
	go fetchProjects(projectRespCh)
	go fetchTasks(tasksRespCh)

	// 2. Searching for projects with [SYNC] prefix in project name
	projects := make([]tc.Project, 0)
	for _, p := range <-projectRespCh {
		if syncPattern.MatchString(p.Name) {
			projects = append(projects, p)
		}
	}

	// 3. Fetching comments for every task in each sync project
	tasks := make([]se.Task, 0)
	go resolveSyncTasks(<-tasksRespCh, projects, tasksCh)
	for t := range tasksCh {
		tasks = append(tasks, t)
	}

	// 4. Merging projects and tasks into sync_entities
	entities := make([]se.SyncEntity, len(projects))
	for _, p := range projects {
		e := se.SyncEntity{Project: p}
		for _, t := range tasks {
			if p.ID == t.Task.ProjectID {
				e.AddTask(t)
			}
		}
		entities = append(entities, e)
	}

	for _, e := range entities {
		log.Print(e)
	}
}

func resolveSyncTasks(tasks []tc.Task, ps []tc.Project, out chan se.Task) {
	defer elapsed("[Elapsed] MAIN#resolveSyncTasks goroutine")()
	var wg sync.WaitGroup
	for _, p := range ps {
		for _, t := range tasks {
			if t.ProjectID == p.ID {
				wg.Add(1)
				go fetchTaskComments(t, out, &wg)
			}
		}
	}
	wg.Wait()
	close(out)
}

func fetchTaskComments(task tc.Task, out chan se.Task, wg *sync.WaitGroup) {
	defer elapsed(fmt.Sprintf("[Elapsed] MAIN#fetchTaskComments goroutine for task_id: %d", task.ID))()
	comments, err := todoistClient.GetComments(task.ID)
	if err != nil {
		log.Fatal(fmt.Sprintf("Fatal [Main] cannot fetch comments for task_id: %d, %v", task.ID, err))
	}
	out <- se.Task{Task: task, Comments: comments}
	wg.Done()
}

func fetchProjects(out chan<- []tc.Project) {
	defer elapsed("[Elapsed] MAIN#fetchProjects goroutine")()
	projects, err := todoistClient.GetProjects()
	if err != nil {
		log.Fatal("Fatal [Main] cannot fetch projects", err)
	}
	out <- projects
	close(out)
}

func fetchTasks(out chan<- []tc.Task) {
	defer elapsed("[Elapsed] MAIN#fetchTasks goroutine")()
	tasks, err := todoistClient.GetTasks()
	if err != nil {
		log.Fatal("Fatal [Main] cannot fetch tasks", err)
	}
	out <- tasks
	close(out)
}

func elapsed(message string) func() {
	start := time.Now()
	return func() { log.Printf("%s took %v\n", message, time.Since(start)) }
}

func checkVars(todoistToken string) {
	log.Print("[Main#init] Checking environment variables")
	if todoistToken == "" {
		log.Fatal("Fatal [Main#checkVars] Cannot resolve TODOIST_TOKEN env variable")
	}
}

repository: Fedor-Bystrov/tt-sync

repo_name: Fedor-Bystrov/tt-sync

extension: go


text:
# Imported from git://git.yoctoproject.org/meta-cloud-services

SUMMARY = "Python parsing module"
HOMEPAGE = "http://pyparsing.wikispaces.com/"
SECTION = "devel/python"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=fb46329938e6bc829b256e37d5c1e31a"

SRCNAME = "pyparsing"

SRC_URI = "http://pypi.python.org/packages/source/p/${SRCNAME}/${SRCNAME}-${PV}.zip"

SRC_URI[md5sum] = "dc7036a6f1ab01c3baed3d97af8d77c0"
SRC_URI[sha256sum] = "7e1766ee747cca79fe172b670fd53ef58fc55d2b1804fd6c66d857abb62d143a"

S = "${WORKDIR}/${SRCNAME}-${PV}"

inherit distutils

branch_name: refs/heads/fido

gha_license_id: MIT

gha_language: BitBake


text:
package message

// PayloadNewChannel is a payload that signals a request for a new SSH channel
type PayloadNewChannel struct {
	ChannelType string `json:"channelType" yaml:"channelType"`
}

// Equals compares two PayloadNewChannel payloads.
func (p PayloadNewChannel) Equals(other Payload) bool {
	p2, ok := other.(PayloadNewChannel)
	if !ok {
		return false
	}
	return p.ChannelType == p2.ChannelType
}

// PayloadNewChannelFailed is a payload that signals that a request for a new channel has failed.
type PayloadNewChannelFailed struct {
	ChannelType string `json:"channelType" yaml:"channelType"`
	Reason      string `json:"reason" yaml:"reason"`
}

// Equals compares two PayloadNewChannelFailed payloads.
func (p PayloadNewChannelFailed) Equals(other Payload) bool {
	p2, ok := other.(PayloadNewChannelFailed)
	if !ok {
		return false
	}
	return p.ChannelType == p2.ChannelType && p.Reason == p2.Reason
}

// PayloadNewChannelSuccessful is a payload that signals that a channel request was successful.
type PayloadNewChannelSuccessful struct {
	ChannelType string `json:"channelType" yaml:"channelType"`
}

// Equals compares two PayloadNewChannelSuccessful payloads.
func (p PayloadNewChannelSuccessful) Equals(other Payload) bool {
	p2, ok := other.(PayloadNewChannelSuccessful)
	if !ok {
		return false
	}
	return p.ChannelType == p2.ChannelType
}

repository: fossabot/auditlog

repo_name: fossabot/auditlog

branch_name: refs/heads/main


text:
DESCRIPTION = "camsource grabs images from a video4linux device and makes them \
available to various plugins for processing or handling."
SECTION = "console/utils"
LICENSE = "GPL"
DEPENDS = "jpeg libxml2"

SRC_URI = "${SOURCEFORGE_MIRROR}/camsource/camsource-${PV}.tar.bz2 \
	   file://gcc34.patch"
S = "${WORKDIR}/camsource-${PV}"

inherit autotools

SRC_URI[md5sum] = "ffd824f13f99011984399fc3b7526c71"
SRC_URI[sha256sum] = "837ba51635e01209dc2886491afbc2e97629bfaf32e73566a1862ea0dc090756"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
DESCRIPTION = "Control application for B.A.T.M.A.N. routing protocol kernel module for multi-hop ad-hoc mesh networks."
HOMEPAGE = "http://www.open-mesh.net/"
SECTION = "console/network"
PRIORITY = "optional"

LICENSE = "CLOSED"

RDEPENDS_${PN} = "kernel-module-batman-adv"
DEPENDS = "libnl"

SRC_URI = "file://batctl-2015.2.tar.gz"
#     file://patch.patch"
SRC_URI[md5sum] = "3db00af0b3cf55136babe06aa666e15f"
#SRC_URI[sha256sum] = "77509ed70232ebc0b73e2fa9471ae13b12d6547d167dda0a82f7a7fad7252c36"

EXTRA_OEMAKE = 'STAGING_INC="${STAGING_INC}"'

do_compile() {
  oe_runmake
}

do_install() {
  install -d ${D}${bindir}
  install -m 0755 batctl ${D}${bindir}
}