Linganisha mbinu

	Utambuzi wa upendeleo wa kijinsia katika NLP	Uchanganuzi wa Hisia
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2017–2018 (seminal benchmarks)	—
Mwanzilishi	Caliskan et al. (2017); Zhao et al. (2018)	—
Aina	NLP bias auditing pipeline	NLP text-classification task
Chanzo asilia	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.
Majina mbadala	Toplumsal Cinsiyet Yanlılığı Tespiti — NLP, bias auditing NLP, WEAT, WinoBias	opinion mining, polarity detection, duygu analizi
Zinazohusiana	5	3
Muhtasari	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.