Social media discourse on feminism: A dataset for sentiment analysis in bangla comments

Bangladesh is a socio-culturally diverse country where perspectives on women's freedom vary significantly. Social media sites are now important places for sharing feminist ideas through public comments and posts. This study offers a detailed collection of 6,830 comments in Bangla about feminism to help analyze public opinion. Most of this data comes from Facebook, with some also from Instagram and Twitter. Data collection involved systematic extraction from public groups and targeted hashtag searches, including (women's rights). Native Bangla speakers meticulously annotated each comment by hand to ensure that it was topical and to identify any abusive language. This manual validation procedure guarantees a high-quality dataset appropriate for the study of online gender-based violence in the Bangla language context, sentiment analysis, and abusive language analysis, among other machine learning and NLP tasks. In addition, comments were divided into three sentiment classes: neutral, negative, and positive. This allowed for thorough analysis of feminist discourse on Bangladeshi social media and supervised learning.

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Publication Details

Journal
Data in Brief
Published
2025-12-24
DOI
https://doi.org/10.1016/j.dib.2025.112383
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Social media discourse on feminism: A dataset for sentiment analysis in bangla comments

Zaid Bin Sajid, Fahim Faisal Sifat, Md. Sumon Hosen, Md. Mijanur Rahman
Data in Brief
Hate Speech and Cyberbullying Detection
article

Social media discourse on feminism: A dataset for sentiment analysis in bangla comments

Zaid Bin Sajid, Fahim Faisal Sifat, Md. Sumon Hosen, Md. Mijanur Rahman
article en

Abstract

Bangladesh is a socio-culturally diverse country where perspectives on women's freedom vary significantly. Social media sites are now important places for sharing feminist ideas through public comments and posts. This study offers a detailed collection of 6,830 comments in Bangla about feminism to help analyze public opinion. Most of this data comes from Facebook, with some also from Instagram and Twitter. Data collection involved systematic extraction from public groups and targeted hashtag searches, including (women's rights). Native Bangla speakers meticulously annotated each comment by hand to ensure that it was topical and to identify any abusive language. This manual validation procedure guarantees a high-quality dataset appropriate for the study of online gender-based violence in the Bangla language context, sentiment analysis, and abusive language analysis, among other machine learning and NLP tasks. In addition, comments were divided into three sentiment classes: neutral, negative, and positive. This allowed for thorough analysis of feminist discourse on Bangladeshi social media and supervised learning.

Data in BriefVol. 64
Southeast University (BD)
Gender equality
Openalex Percentile: Top 18%
Hate Speech and Cyberbullying Detection
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Social media discourse on feminism: A dataset for sentiment analysis in bangla comments — Zaid Bin Sajid, Fahim Faisal Sifat, et al. · Data in Brief (2025) | TGRS Research Map | TGRS