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Runtime error
File size: 2,945 Bytes
8e9a7ea 2b93e1d 8e9a7ea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import praw\n",
"import pandas as pd\n",
"\n",
"reddit= praw.Reddit(client_id=\"Q1w42RHhLq2fgwljAk_k-Q\",\t\t # your client id\n",
"\t\t\t\t\tclient_secret=\"enUJfFthiZRynGfPQtoK1nCxRer2Dw\",\t # your client secret\n",
" usernme = \"xl395\", #profile username\n",
" password = \"12xiao34quanAria!\", #profile password\n",
"\t\t\t\t\tuser_agent=\"706_post\")\t # your user agent"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "expected ':' (3580341109.py, line 8)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[35], line 8\u001b[0;36m\u001b[0m\n\u001b[0;31m def extract_comments(input_url)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m expected ':'\n"
]
}
],
"source": [
"from praw.models import MoreComments\n",
"from transformers import pipeline\n",
"from transformers import DistilBertTokenizerFast\n",
"classifier = pipeline(\"sentiment-analysis\", model=\"michellejieli/NSFW_text_classifier\")\n",
"\n",
"input_url = \"https://www.reddit.com/r/europe/comments/r0hthg/sweden_is_taking_the_lead_to_persuade_the_rest_of/\"\n",
"\n",
"def extract_comments(input_url):\n",
" submission = reddit.submission(url=input_url)\n",
" posts_dict = {\"Post text\":[],}\n",
" # posts_dict = {\"Post text\":[], \"class\": []}\n",
" for top_level_comment in submission.comments:\n",
" if isinstance(top_level_comment, MoreComments):\n",
" continue\n",
" \n",
" posts_dict[\"Post text\"].append(top_level_comment.body)\n",
" #post_dict[\"class\"].append(classifier(top_level_comment.body))\n",
" df = pd.DataFrame(posts_dict)\n",
" return df "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_process = extract_comments(input_url)\n",
"\n",
"# find how many are NSFW - output message"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4 (main, Nov 29 2022, 20:00:25) [GCC 9.4.0]"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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