# yangheng95/PyABSA

Sentiment Analysis, Text Classification, Text Augmentation,  Text Adversarial defense, etc.;

Repository: https://github.com/yangheng95/PyABSA
Canonical: https://ross.abutalabs.com/products/pyabsa
Homepage: https://pyabsa.readthedocs.io
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: aspect-based-sentiment-analysis, aspect-term-extraction, lcf-bert, pytorch, pyabsa, adversarial, regression, aspect-sentiment-triplet-extraction
Last push: 2026-08-26T00:08:26+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 8, longevity 100
- inputs: {"age_days": 2569, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1102, forks 174 (observed 2026-08-28T04:03:35.893834+00:00)

## What it is
PyABSA is a PyTorch-based library providing state-of-the-art models for aspect-based sentiment analysis, including aspect term extraction, aspect polarity classification, and text classification. It also supports text augmentation and adversarial defense for NLP models.

## Use cases
- extract aspect terms from product reviews
- classify sentiment polarity toward specific aspects in text
- train and fine-tune aspect-based sentiment analysis models
- perform general text classification
- augment training data for NLP tasks
- defend NLP models against adversarial attacks

## When to choose
- you need aspect-level sentiment analysis rather than document-level sentiment
- you want pretrained state-of-the-art ATE/APC models with a simple Python API
- you are working in PyTorch and need reproducible ABSA baselines

## When to avoid
- you only need simple overall document sentiment scores without aspect extraction
- you need a production serving system rather than a modeling library
- your project is not Python/PyTorch based

## Facets
- artifact type: library
- maturity: active
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: python, cross-platform
- tags: aspect-based-sentiment-analysis, aspect-term-extraction, pytorch, text-classification, adversarial-defense, text-augmentation, natural-language-processing

## Member repositories
- yangheng95/PyABSA (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.893834+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 2026-08-30T06:45:40.248298+00:00, confidence not recorded.
  - registry_pypi: https://pypi.org/pypi/pyabsa/json (fetched 2026-08-29T12:49:22.758002+00:00, sha 49f1fd6a99b9)
- Data as of 2026-08-30T08:39:29.467469+00:00.