80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
import pandas as pd
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import re
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from collections import Counter
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from itertools import islice
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class LinguisticAnalysis:
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def __init__(self, word_exclusions: set[str]):
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self.word_exclusions = word_exclusions
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def _tokenize(self, text: str):
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tokens = re.findall(r"\b[a-z]{3,}\b", text)
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return [t for t in tokens if t not in self.word_exclusions]
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def _clean_text(self, text: str) -> str:
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text = re.sub(r"http\S+", "", text) # remove URLs
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text = re.sub(r"www\S+", "", text)
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text = re.sub(r"&\w+;", "", text) # remove HTML entities
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text = re.sub(r"\bamp\b", "", text) # remove stray amp
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text = re.sub(r"\S+\.(jpg|jpeg|png|webp|gif)", "", text)
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return text
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def word_frequencies(self, df: pd.DataFrame, limit: int = 100) -> list[dict]:
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texts = df["content"].dropna().astype(str).str.lower()
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words = []
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for text in texts:
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tokens = re.findall(r"\b[a-z]{3,}\b", text)
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words.extend(w for w in tokens if w not in self.word_exclusions)
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counts = Counter(words)
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word_frequencies = (
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pd.DataFrame(counts.items(), columns=["word", "count"])
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.sort_values("count", ascending=False)
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.head(limit)
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.reset_index(drop=True)
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)
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return word_frequencies.to_dict(orient="records")
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def ngrams(self, df: pd.DataFrame, n=2, limit=100):
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texts = df["content"].dropna().astype(str).apply(self._clean_text).str.lower()
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all_ngrams = []
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for text in texts:
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tokens = re.findall(r"\b[a-z]{3,}\b", text)
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# stop word removal causes strange behaviors in ngrams
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# tokens = [w for w in tokens if w not in self.word_exclusions]
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ngrams = zip(*(islice(tokens, i, None) for i in range(n)))
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all_ngrams.extend([" ".join(ng) for ng in ngrams])
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counts = Counter(all_ngrams)
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return (
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pd.DataFrame(counts.items(), columns=["ngram", "count"])
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.sort_values("count", ascending=False)
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.head(limit)
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.to_dict(orient="records")
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)
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def lexical_diversity(self, df: pd.DataFrame) -> dict:
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tokens = (
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df["content"].fillna("").astype(str).str.lower()
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.str.findall(r"\b[a-z]{2,}\b")
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.explode()
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)
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tokens = tokens[~tokens.isin(self.word_exclusions)]
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total = max(len(tokens), 1)
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unique = int(tokens.nunique())
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return {
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"total_tokens": total,
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"unique_tokens": unique,
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"ttr": round(unique / total, 4),
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}
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