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feat: top most used words per user in user analysis endpoint

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Dylan De Faoite
2026-02-02 15:14:56 +00:00
parent fe257ece5c
commit 8b25b7bd09
1 changed files with 9 additions and 2 deletions
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11
server/stat_gen.py
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@@ -43,7 +43,7 @@ class StatGen:
tokens = re.findall(r"\b[a-z]{3,}\b", text) tokens = re.findall(r"\b[a-z]{3,}\b", text)
return [t for t in tokens if t not in EXCLUDE_WORDS] return [t for t in tokens if t not in EXCLUDE_WORDS]
def _vocab_richness_per_user(self, min_words: int = 20) -> dict: def _vocab_richness_per_user(self, min_words: int = 20, top_most_used_words: int = 100) -> list:
df = self.df.copy() df = self.df.copy()
df["content"] = df["content"].fillna("").astype(str).str.lower() df["content"] = df["content"].fillna("").astype(str).str.lower()
df["tokens"] = df["content"].apply(self._tokenize) df["tokens"] = df["content"].apply(self._tokenize)
@@ -64,6 +64,12 @@ class StatGen:
vocab_richness = unique_words / total_words vocab_richness = unique_words / total_words
avg_words = total_words / max(events, 1) avg_words = total_words / max(events, 1)
counts = Counter(all_tokens)
top_words = [
{"word": w, "count": int(c)}
for w, c in counts.most_common(top_most_used_words)
]
rows.append({ rows.append({
"author": author, "author": author,
"events": int(events), "events": int(events),
@@ -71,11 +77,12 @@ class StatGen:
"unique_words": int(unique_words), "unique_words": int(unique_words),
"vocab_richness": round(vocab_richness, 3), "vocab_richness": round(vocab_richness, 3),
"avg_words_per_event": round(avg_words, 2), "avg_words_per_event": round(avg_words, 2),
"top_words": top_words
}) })
rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True) rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
return {"vocab_richness": rows} return rows
## Public ## Public
def time_analysis(self) -> pd.DataFrame: def time_analysis(self) -> pd.DataFrame: