Vertaile menetelmiä

	Sukupuoliharhan havaitseminen luonnollisen kielen käsittelyssä (NLP)	Sentiment Analysis
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2017–2018 (seminal benchmarks)	—
Kehittäjä	Caliskan et al. (2017); Zhao et al. (2018)	—
Tyyppi	NLP bias auditing pipeline	NLP text-classification task
Alkuperäislähde	Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Rinnakkaisnimet	Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias	opinion mining, polarity detection, duygu analizi
Liittyvät	5	3
Tiivistelmä	Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora and language models. Grounded in benchmarks established by Caliskan et al. (2017) with the Word Embedding Association Test (WEAT) and Zhao et al. (2018) with the WinoBias dataset, these methods produce quantitative evidence of gender bias rather than qualitative impressions. They are widely applied in ethical AI research, media analysis, and fairness auditing of machine-learning systems.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.