# chaoming0625/SentimentPolarityAnalysis

情感极性分析repository1：基于情感词典、k-NN、Bayes、最大熵、SVM的情感极性分析。

Repository: https://github.com/chaoming0625/SentimentPolarityAnalysis
Canonical: https://ross.abutalabs.com/products/sentimentpolarityanalysis
Language: Python
License Family: other
Last push: 2018-08-23T13:09:30+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3746, "days_push": 2932, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1267, forks 332 (observed 2026-08-28T04:04:11.299246+00:00)

## What it is
A Python library for Chinese sentiment polarity analysis offering both lexicon-based scoring and classic ML classifiers (k-NN, Bayes, maximum entropy, SVM) with chi-square feature extraction. It can analyze individual sentences or batch files and report per-clause sentiment explanations.

## Use cases
- analyze sentiment polarity of Chinese sentences
- classify restaurant reviews as positive or negative
- compare k-NN, Bayes, maxent and SVM text classifiers
- extract sentiment features with chi-square test
- score sentences using a sentiment dictionary
- batch sentiment analysis of text files

## When to choose
- you need lexicon-based, explainable sentiment scoring for Chinese text
- you want to experiment with classic ML classifiers on sentiment datasets
- you need per-clause sentiment breakdowns with conjunction handling

## When to avoid
- you need actively maintained code or a license
- you need modern transformer-based sentiment models
- you need non-Chinese language support

## Facets
- artifact type: library
- maturity: abandoned
- function: nlp, machine-learning, parser
- domain: machine-learning, data-science
- platform: python
- tags: sentiment-analysis, sentiment-lexicon, text-classification, chinese-nlp, knn, naive-bayes, maximum-entropy, svm, feature-extraction, natural-language-processing

## Member repositories
- chaoming0625/SentimentPolarityAnalysis (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:11.299246+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:03:41.871591+00:00, confidence not recorded.
  - readme: https://github.com/chaoming0625/SentimentPolarityAnalysis (fetched 2026-08-28T04:04:11.299246+00:00, sha 15ad1c94861a)
- Data as of 2026-08-30T08:39:29.467469+00:00.