140 lines
5.4 KiB
Python
140 lines
5.4 KiB
Python
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.summary import SummaryAnalysis
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from server.analysis.temporal import TemporalAnalysis
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from server.analysis.user import UserAnalysis
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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.summary_analysis = SummaryAnalysis()
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self.user_analysis = UserAnalysis(EXCLUDE_WORDS)
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## Private Methods
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def _prepare_filtered_df(self, df: pd.DataFrame, filters: dict | None = None) -> pd.DataFrame:
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filters = filters or {}
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filtered_df = df.copy()
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search_query = filters.get("search_query", None)
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start_date_filter = filters.get("start_date", None)
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end_date_filter = filters.get("end_date", None)
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data_source_filter = filters.get("data_sources", None)
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if search_query:
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mask = filtered_df["content"].str.contains(
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search_query, case=False, na=False
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) | filtered_df["author"].str.contains(search_query, case=False, na=False)
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# Only include title if the column exists
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if "title" in filtered_df.columns:
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mask = mask | filtered_df["title"].str.contains(
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search_query, case=False, na=False, regex=False
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)
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filtered_df = filtered_df[mask]
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if start_date_filter:
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filtered_df = filtered_df[(filtered_df["dt"] >= start_date_filter)]
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if end_date_filter:
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filtered_df = filtered_df[(filtered_df["dt"] <= end_date_filter)]
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if data_source_filter:
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filtered_df = filtered_df[filtered_df["source"].isin(data_source_filter)]
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return filtered_df
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## Public Methods
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def filter_dataset(self, df: pd.DataFrame, filters: dict | None = None) -> list[dict]:
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return self._prepare_filtered_df(df, filters).to_dict(orient="records")
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def temporal(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return {
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"events_per_day": self.temporal_analysis.posts_per_day(filtered_df),
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"weekday_hour_heatmap": self.temporal_analysis.heatmap(filtered_df),
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}
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def linguistic(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return {
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"word_frequencies": self.linguistic_analysis.word_frequencies(filtered_df),
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"common_two_phrases": self.linguistic_analysis.ngrams(filtered_df),
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"common_three_phrases": self.linguistic_analysis.ngrams(filtered_df, n=3),
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"lexical_diversity": self.linguistic_analysis.lexical_diversity(filtered_df)
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}
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def emotional(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return {
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"average_emotion_by_topic": self.emotional_analysis.avg_emotion_by_topic(filtered_df),
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"overall_emotion_average": self.emotional_analysis.overall_emotion_average(filtered_df),
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"dominant_emotion_distribution": self.emotional_analysis.dominant_emotion_distribution(filtered_df),
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"emotion_by_source": self.emotional_analysis.emotion_by_source(filtered_df)
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}
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def user(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return {
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"top_users": self.user_analysis.top_users(filtered_df),
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"users": self.user_analysis.per_user_analysis(filtered_df)
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}
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def interactional(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return {
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"top_interaction_pairs": self.interaction_analysis.top_interaction_pairs(filtered_df, top_n=100),
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"interaction_graph": self.interaction_analysis.interaction_graph(filtered_df),
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"conversation_concentration": self.interaction_analysis.conversation_concentration(filtered_df)
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}
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def cultural(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return {
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"identity_markers": self.cultural_analysis.get_identity_markers(filtered_df),
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"stance_markers": self.cultural_analysis.get_stance_markers(filtered_df),
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"avg_emotion_per_entity": self.cultural_analysis.get_avg_emotions_per_entity(filtered_df)
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}
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def summary(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
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filtered_df = self._prepare_filtered_df(df, filters)
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return self.summary_analysis.summary(filtered_df)
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