142 lines
5.2 KiB
Python
142 lines
5.2 KiB
Python
import datetime
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import nltk
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import pandas as pd
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from nltk.corpus import stopwords
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from server.analysis.cultural import CulturalAnalysis
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from server.analysis.emotional import EmotionalAnalysis
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from server.analysis.interactional import InteractionAnalysis
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from server.analysis.linguistic import LinguisticAnalysis
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from server.analysis.temporal import TemporalAnalysis
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DOMAIN_STOPWORDS = {
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"www",
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"https",
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"http",
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"boards",
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"boardsie",
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"comment",
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"comments",
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"discussion",
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"thread",
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"post",
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"posts",
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"would",
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"get",
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"one",
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}
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nltk.download("stopwords")
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EXCLUDE_WORDS = set(stopwords.words("english")) | DOMAIN_STOPWORDS
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class StatGen:
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def __init__(self) -> None:
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self.temporal_analysis = TemporalAnalysis()
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self.emotional_analysis = EmotionalAnalysis()
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self.interaction_analysis = InteractionAnalysis(EXCLUDE_WORDS)
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self.linguistic_analysis = LinguisticAnalysis(EXCLUDE_WORDS)
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self.cultural_analysis = CulturalAnalysis()
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self.search_query = ""
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self.start_date_filter = None
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self.end_date_filter = None
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self.data_source_filters = set()
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## Private Methods
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def _prepare_filtered_df(self, df: pd.DataFrame) -> pd.DataFrame:
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filtered_df = df.copy()
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if self.search_query:
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mask = (
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filtered_df["content"].str.contains(self.search_query, case=False, na=False)
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| filtered_df["author"].str.contains(self.search_query, case=False, na=False).fillna(False)
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| filtered_df["title"].str.contains(self.search_query, case=False, na=False, regex=False).fillna(False)
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)
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filtered_df = filtered_df[mask]
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if self.start_date_filter and self.end_date_filter:
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filtered_df = filtered_df[
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(filtered_df["dt"] >= self.start_date_filter) & (filtered_df["dt"] <= self.end_date_filter)
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]
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if self.data_source_filters:
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enabled_sources = [src for src, enabled in self.data_source_filters.items() if enabled]
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if enabled_sources:
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filtered_df = filtered_df[filtered_df["source"].isin(enabled_sources)]
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return filtered_df
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## Public Methods
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def get_time_analysis(self, df: pd.DataFrame) -> dict:
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return {
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"events_per_day": self.temporal_analysis.posts_per_day(df),
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"weekday_hour_heatmap": self.temporal_analysis.heatmap(df),
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}
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def get_content_analysis(self, df: pd.DataFrame) -> dict:
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return {
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"word_frequencies": self.linguistic_analysis.word_frequencies(df),
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"common_two_phrases": self.linguistic_analysis.ngrams(df),
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"common_three_phrases": self.linguistic_analysis.ngrams(df, n=3),
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"average_emotion_by_topic": self.emotional_analysis.avg_emotion_by_topic(df),
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"reply_time_by_emotion": self.temporal_analysis.avg_reply_time_per_emotion(df),
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}
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def get_user_analysis(self, df: pd.DataFrame) -> dict:
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return {
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"top_users": self.interaction_analysis.top_users(df),
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"users": self.interaction_analysis.per_user_analysis(df),
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"interaction_graph": self.interaction_analysis.interaction_graph(df),
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}
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def get_interactional_analysis(self, df: pd.DataFrame) -> dict:
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return {
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"average_thread_depth": self.interaction_analysis.average_thread_depth(df),
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"average_thread_length_by_emotion": self.interaction_analysis.average_thread_length_by_emotion(df),
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}
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def get_cultural_analysis(self, df: pd.DataFrame) -> dict:
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return {
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"identity_markers": self.cultural_analysis.get_identity_markers(df),
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"stance_markers": self.cultural_analysis.get_stance_markers(df),
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"entity_salience": self.cultural_analysis.get_avg_emotions_per_entity(df),
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}
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def summary(self, df: pd.DataFrame) -> dict:
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total_posts = (df["type"] == "post").sum()
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total_comments = (df["type"] == "comment").sum()
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events_per_user = df.groupby("author").size()
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return {
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"total_events": int(len(df)),
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"total_posts": int(total_posts),
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"total_comments": int(total_comments),
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"unique_users": int(events_per_user.count()),
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"comments_per_post": round(total_comments / max(total_posts, 1), 2),
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"lurker_ratio": round((events_per_user == 1).mean(), 2),
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"time_range": {
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"start": int(df["dt"].min().timestamp()),
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"end": int(df["dt"].max().timestamp()),
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},
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"sources": df["source"].dropna().unique().tolist(),
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}
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def filter_by_query(self, search_query: str) -> None:
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self.search_query = search_query
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def set_time_range(self, start: datetime.datetime, end: datetime.datetime) -> None:
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self.start_date_filter = start
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self.end_date_filter = end
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def filter_data_sources(self, data_sources: set) -> None:
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self.data_source_filters = data_sources
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def reset_dataset(self) -> None:
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self.search_query = ""
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self.start_date_filter = None
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self.end_date_filter = None
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self.data_source_filters = set()
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