778 lines
179 KiB
Plaintext
778 lines
179 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd \n",
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"import spacy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
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"metadata": {},
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"outputs": [],
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"source": [
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"phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/050825_coref-rel-final.csv\"\n",
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"phab_df = pd.read_csv(phab_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d449164e-1d28-4580-9eb1-f0f69978f114",
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"metadata": {},
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"outputs": [],
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"source": [
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"#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
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"#phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
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"\n",
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"#cleaning df\n",
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"#phab_df['id'] = phab_df.index + 1\n",
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"#may have to build out the reply_to column \n",
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"#phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
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"#phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
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"\n",
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"#phab_df = phab_df.rename(columns={\n",
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"# 'AuthorPHID': 'speaker',\n",
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"# 'TaskPHID': 'conversation_id',\n",
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"# 'WMFaffil':'meta.affil',\n",
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"# 'isGerrit': 'meta.gerrit'\n",
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"#})\n",
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"\n",
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"# after 12-1-2012 before 12-1-2013\n",
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"#phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
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"#filtered_phab_df = phab_df[(phab_df['date_created'] < 1385856000) & (phab_df['date_created'] > 1354320000)]\n",
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"#filtered_phab_df = phab_df[(phab_df['date_created'] < 1381691276) & (phab_df['date_created'] > 1379975444)]\n",
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"\n",
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"#removing headless conversations\n",
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"task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
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"headed_task_phids = task_phab_df['conversation_id'].unique()\n",
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"filtered_phab_df = phab_df[phab_df['conversation_id'].isin(headed_task_phids)]\n",
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"\n",
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"#removing gerrit comments \n",
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"#mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
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"\n",
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"'''\n",
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"# filter out the sourceforge migration \n",
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"# Originally from: http://sourceforge.net in the task task_summary\n",
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"migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\n",
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"\n",
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"#cut down to only the data that is relevant (mentions http)\n",
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"relevant_conversation_ids = task_phab_df[\n",
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" task_phab_df['comment_text'].apply(http_relevant) |\n",
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" task_phab_df['task_title'].apply(http_relevant)\n",
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"]['conversation_id'].unique()\n",
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"\n",
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"task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
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"mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
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"\n",
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"task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
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"mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
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"'''\n",
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"#comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
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"#task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
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"comment_phab_df = filtered_phab_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Unique conversation_ids: 2281\n",
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"Unique ids: 14490\n",
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"Unique speakers: 634\n"
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]
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}
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],
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"source": [
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"unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
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"unique_ids = len(comment_phab_df['id'].unique())\n",
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"unique_speakers = len(comment_phab_df['speaker'].unique())\n",
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"\n",
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"print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
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"print(f\"Unique ids: {unique_ids}\")\n",
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"print(f\"Unique speakers: {unique_speakers}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "d226d781-b002-4842-a3ae-92d4851a5878",
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"\n",
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"def preprocess_text(text):\n",
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" text = str(text)\n",
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" text = text.replace('*', ' ')\n",
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" text = text.replace('-', ' ')\n",
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" text = re.sub(r'http\\S+', '', text)\n",
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" return text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
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"metadata": {},
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"outputs": [],
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"source": [
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"comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
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"metadata": {},
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"outputs": [],
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"source": [
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"comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
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"metadata": {},
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"outputs": [],
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"source": [
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"nlp = spacy.load(\"en_core_web_sm\")\n",
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"\n",
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"def extract_dependency_tree(text):\n",
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" doc = nlp(text)\n",
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" dependency_trees = []\n",
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" \n",
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" for sentence in doc.sents:\n",
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" for token in sentence:\n",
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" token_info = (\n",
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" token.text, \n",
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" token.lemma_, \n",
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" token.dep_, \n",
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" token.head.text, \n",
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" list(token.ancestors), \n",
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" list(token.subtree), \n",
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" list(token.children)\n",
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" )\n",
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" dependency_trees.append(token_info)\n",
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" \n",
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" return dependency_trees"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
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"metadata": {},
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"outputs": [],
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"source": [
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"comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
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"metadata": {},
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"outputs": [],
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"source": [
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"comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"#get VAD scores\n",
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"import numpy as np\n",
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"#https://saifmohammad.com/WebPages/nrc-vad.html\n",
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"column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
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"vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
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"vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
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"\n",
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"def vad_scoring(dependency_tree):\n",
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" valence = []\n",
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" arousal = []\n",
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" dominance = []\n",
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" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
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" if lemma in vad_dict:\n",
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" valence.append(vad_dict[lemma]['Valence'])\n",
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" arousal.append(vad_dict[lemma]['Arousal'])\n",
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" dominance.append(vad_dict[lemma]['Domination'])\n",
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"\n",
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" # Compute average scores across the comment\n",
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" avg_valence = np.mean(valence) if valence else 0\n",
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" avg_arousal = np.mean(arousal) if arousal else 0\n",
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" avg_dominance = np.mean(dominance) if dominance else 0\n",
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"\n",
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" return [avg_valence, avg_arousal, avg_dominance]\n",
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"\n",
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"def dominance_prevail(dependency_tree):\n",
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" dominant_words = 0 \n",
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" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
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" if lemma in vad_dict:\n",
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" if vad_dict[lemma]['Domination'] >= 0.75:\n",
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" dominant_words += 1\n",
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" if vad_dict[lemma]['Domination'] <= 0.25:\n",
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" dominant_words += 1\n",
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" return dominant_words\n",
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"\n",
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"def arousal_prevail(dependency_tree):\n",
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" arousal_words = 0 \n",
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" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
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" if lemma in vad_dict:\n",
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" if vad_dict[lemma]['Arousal'] >= 0.75:\n",
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" arousal_words += 1\n",
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" if vad_dict[lemma]['Arousal'] <= 0.25:\n",
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" arousal_words += 1\n",
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" return arousal_words\n",
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"\n",
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"def valence_prevail(dependency_tree):\n",
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" valence_words = 0 \n",
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" for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
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" if lemma in vad_dict:\n",
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" if vad_dict[lemma]['Valence'] >= 0.75:\n",
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" valence_words += 1\n",
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" if vad_dict[lemma]['Valence'] <= 0.25:\n",
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" valence_words += 1\n",
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" return valence_words\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "828fb57a-e152-42ef-9c60-660648898532",
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"metadata": {},
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"outputs": [],
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"source": [
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"#establishing per-comment VAD scores \n",
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"comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
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"comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
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"comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
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"comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "27e47f6f-0257-4b70-b222-e91ef888c900",
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"metadata": {},
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"outputs": [],
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"source": [
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"comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
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"comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "1889034d-bc93-495f-bdc4-961d193d3e08",
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"metadata": {},
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"outputs": [],
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"source": [
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"def token_http_relevant(word):\n",
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" # expanded dictionary for relevancy\n",
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" # http, login, SSL, TLS, certificate \n",
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" if \"://\" not in word.lower():\n",
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" #http\n",
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" if \"http\" in word.lower():\n",
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" return True\n",
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" #login\n",
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" if \"login\" in word.lower():\n",
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" return True\n",
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" #ssl\n",
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" if \"ssl\" in word.lower():\n",
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" return True\n",
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" #tls\n",
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" if \"tls\" in word.lower():\n",
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" return True\n",
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" #cert\n",
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" if word.lower().startswith(\"cert\") and not word.lower().startswith(\"certain\"):\n",
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" return True\n",
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" return False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "09ddcbfc-b856-40ca-ad61-13577795d94b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import datetime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# expand the dependency parser \n",
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"dependency_relations = []\n",
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"resolved_dependency_relations = []\n",
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"\n",
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"for index, row in comment_phab_df.iterrows():\n",
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" text = row['comment_text']\n",
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" timestamp = row['timestamp']\n",
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" comment_id = row['id']\n",
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" conversation_id = row['conversation_id']\n",
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" WMFaffil = row['meta.affil']\n",
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" \n",
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" for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
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" dependency_relations.append({\n",
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" 'comment_id': comment_id,\n",
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" 'timestamp': timestamp,\n",
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" 'wmfAffil':WMFaffil,\n",
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" 'token': token,\n",
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" 'dependency': dep,\n",
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" 'head': head,\n",
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" 'depth': len(list(ancestors)), \n",
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" 'children': len(list(children)) \n",
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" })\n",
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" \n",
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" for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
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" resolved_dependency_relations.append({\n",
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" 'comment_id': comment_id,\n",
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" 'timestamp': timestamp,\n",
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" 'wmfAffil':WMFaffil,\n",
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" 'token': token,\n",
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" 'dependency': dep,\n",
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" 'head': head,\n",
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" 'depth': len(list(ancestors)), \n",
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" 'children': len(list(children)) \n",
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" })\n",
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"\n",
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"resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations) \n",
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"dependency_relations_df = pd.DataFrame(dependency_relations)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "82498686-14f4-40c8-9e33-27b31f115b47",
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"metadata": {},
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"outputs": [],
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"source": [
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"#now analysis/plotting \n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from matplotlib.gridspec import GridSpec"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x14ca72b957f0>"
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]
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},
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"execution_count": 35,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1333.5x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
|
||
"plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
|
||
"plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
|
||
"plot2.fig.subplots_adjust(top=0.9) # Adjust subplots to make room for the title\n",
|
||
"plot2.add_legend(title=\"Comment publication timestamp:\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "2274795e-c64d-43e4-b0f5-a19b5b8ba2c8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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"<style scoped>\n",
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|
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
" <td>do</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>1708749 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" comment_id timestamp wmfAffil token dependency \\\n",
|
||
"0 243215 2014-09-24 06:48:00+00:00 False User dep \n",
|
||
"1 243215 2014-09-24 06:48:00+00:00 False : punct \n",
|
||
"2 243215 2014-09-24 06:48:00+00:00 False NickK nsubj \n",
|
||
"3 243215 2014-09-24 06:48:00+00:00 False reported ROOT \n",
|
||
"4 243215 2014-09-24 06:48:00+00:00 False in prep \n",
|
||
"... ... ... ... ... ... \n",
|
||
"1708744 406877 2014-10-17 09:17:17+00:00 False n't neg \n",
|
||
"1708745 406877 2014-10-17 09:17:17+00:00 False really advmod \n",
|
||
"1708746 406877 2014-10-17 09:17:17+00:00 False do ROOT \n",
|
||
"1708747 406877 2014-10-17 09:17:17+00:00 False much dobj \n",
|
||
"1708748 406877 2014-10-17 09:17:17+00:00 False . punct \n",
|
||
"\n",
|
||
" head depth children \n",
|
||
"0 reported 1 0 \n",
|
||
"1 reported 1 0 \n",
|
||
"2 reported 1 0 \n",
|
||
"3 reported 0 7 \n",
|
||
"4 reported 1 1 \n",
|
||
"... ... ... ... \n",
|
||
"1708744 do 1 0 \n",
|
||
"1708745 do 1 0 \n",
|
||
"1708746 do 0 9 \n",
|
||
"1708747 do 1 0 \n",
|
||
"1708748 do 1 0 \n",
|
||
"\n",
|
||
"[1708749 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"resolved_dependency_relations_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_40267/3864244444.py:8: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
|
||
" filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:9: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
|
||
" resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:24: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" filtered_dependencies['timestamp'] = pd.to_datetime(filtered_dependencies['timestamp'], utc=True)\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:25: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
|
||
" filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:25: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:46: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" resolved_filtered_dependencies['timestamp'] = pd.to_datetime(resolved_filtered_dependencies['timestamp'], utc=True)\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:47: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
|
||
" resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
|
||
"/tmp/ipykernel_40267/3864244444.py:47: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1200x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
|
||
"pattern = r'\\b(contributor|community|volunteer)\\b'\n",
|
||
"#pattern = r'\\b(WMF|Foundation|Wikimedia)\\b'\n",
|
||
"#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
|
||
"#pattern = r'\\b(http|https)\\b'\n",
|
||
"#pattern = r'\\b(auth)\\b'\n",
|
||
"\n",
|
||
"filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
|
||
"resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 8))\n",
|
||
"gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
|
||
"\n",
|
||
"# Main plot: Token depth by timestamp\n",
|
||
"'''\n",
|
||
"ax0 = plt.subplot(gs[0])\n",
|
||
"sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
|
||
"ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
|
||
"ax0.set_xlabel('')\n",
|
||
"ax0.set_ylabel('Dependency Type')\n",
|
||
"ax0.legend().set_visible(False)\n",
|
||
"'''\n",
|
||
"# Calculate the median depth over time\n",
|
||
"filtered_dependencies['timestamp'] = pd.to_datetime(filtered_dependencies['timestamp'], utc=True)\n",
|
||
"filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
|
||
"median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
|
||
"\n",
|
||
"wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
|
||
"#wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
|
||
"\n",
|
||
"other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
|
||
"#other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
|
||
"\n",
|
||
"# Plot the median depth over time\n",
|
||
"ax0 = plt.subplot(gs[0])\n",
|
||
"#sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
|
||
"sns.scatterplot(data=wmf_filtered_dependencies, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
|
||
"#sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
|
||
"sns.scatterplot(data=other_filtered_dependencies, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
|
||
"#sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
|
||
"ax0.set_title(f'Depth of {pattern} in Phabricator Sentence Dependency Trees')\n",
|
||
"ax0.set_ylabel('Median Depth')\n",
|
||
"ax0.set_xlabel('')\n",
|
||
"\n",
|
||
"# Calculate the median depth over time\n",
|
||
"resolved_filtered_dependencies['timestamp'] = pd.to_datetime(resolved_filtered_dependencies['timestamp'], utc=True)\n",
|
||
"resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
|
||
"resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
|
||
"\n",
|
||
"resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] == True]\n",
|
||
"#resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
|
||
"\n",
|
||
"resolved_other_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] != True]\n",
|
||
"#resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
|
||
"\n",
|
||
"# Plot the median depth over time\n",
|
||
"ax1 = plt.subplot(gs[1])\n",
|
||
"#sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
|
||
"sns.scatterplot(data=resolved_wmf_filtered_dependencies, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
|
||
"#sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
|
||
"sns.scatterplot(data=resolved_other_filtered_dependencies, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
|
||
"#sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
|
||
"ax1.set_title(f'Depth of {pattern} in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
|
||
"ax1.set_ylabel('Median Depth')\n",
|
||
"ax1.set_xlabel('')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"#plt.show()\n",
|
||
"\n",
|
||
"#plt.savefig('031625_VE_depth_fig.png')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "c3e11af2-388a-4112-a71b-82c9fd6d5886",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.11.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|