# The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases arXiv: https://arxiv.org/abs/2401.10535 ยท HTML: https://mlsanity.com/paper/2401.10535/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 53 of its 317 references that are in the map. - First posted on arXiv 19 Jan 2024. ## Builds on (most cited first, 30 of 53) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021) - [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019) - [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020) - [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020) - [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022) - [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022) - [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018) - [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022) - [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://mlsanity.com/paper/2205.03401/) (2022) - [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019) - [Understanding and Evaluating Racial Biases in Image Captioning](https://mlsanity.com/paper/2106.08503/) (2021) - [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020) - [Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion](https://mlsanity.com/paper/2203.13224/) (2022) - [Discrimination in Online Ad Delivery](https://mlsanity.com/paper/1301.6822/) (2013) - [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020) - [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022) - [Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development](https://mlsanity.com/paper/2108.04308/) (2021) - [Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://mlsanity.com/paper/2106.14574/) (2021) - [Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://mlsanity.com/paper/2203.08383/) (2022) - [Perturbation Sensitivity Analysis to Detect Unintended Model Biases](https://mlsanity.com/paper/1910.04210/) (2019) - [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017) - [Re-contextualizing Fairness in NLP: The Case of India](https://mlsanity.com/paper/2209.12226/) (2022) - [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023) - [Knowing More About Questions Can Help: Improving Calibration in Question Answering](https://mlsanity.com/paper/2106.01494/) (2021) - [Getting MoRE out of Mixture of Language Model Reasoning Experts](https://mlsanity.com/paper/2305.14628/) (2023) ## Built on by (most cited first, 16 of 16) - [Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach](https://mlsanity.com/paper/2407.14779/) (2024) - [From Melting Pots to Misrepresentations: Exploring Harms in Generative AI](https://mlsanity.com/paper/2403.10776/) (2024) - [IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding](https://mlsanity.com/paper/2501.15747/) (2025) - ['Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs](https://mlsanity.com/paper/2409.13484/) (2024) - [Cross-Lingual Sentiment Misalignment: Auditing Multilingual Language Models for Inversion Risk, Dialectal Representation, and Affective Stability](https://mlsanity.com/paper/2602.17469/) (2026) - [Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions](https://mlsanity.com/paper/2501.16748/) (2025) - [Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysis](https://mlsanity.com/paper/2405.05758/) (2024) - [BengaliMoralBench: A Benchmark for Auditing Moral Reasoning in Large Language Models within Bengali Language and Culture](https://mlsanity.com/paper/2511.03180/) (2025) - [Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languages](https://mlsanity.com/paper/2501.13836/) (2025) - [BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communities](https://mlsanity.com/paper/2506.06813/) (2025) - [How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language](https://mlsanity.com/paper/2506.06816/) (2025) - [Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy](https://mlsanity.com/paper/2508.01486/) (2025) - [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026) - [Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models](https://mlsanity.com/paper/2606.00039/) (2026) - [Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities](https://mlsanity.com/paper/2606.13397/) (2026) - [Language Models for Portuguese: A Systematic Mapping Study](https://mlsanity.com/paper/2608.18138/) (2026) ## Abstract (arXiv) While colonization has sociohistorically impacted people's identities across various dimensions, those colonial values and biases continue to be perpetuated by sociotechnical systems. One category of sociotechnical systems--sentiment analysis tools--can also perpetuate colonial values and bias, yet less attention has been paid to how such tools may be complicit in perpetuating coloniality, although they are often used to guide various practices (e.g., content moderation). In this paper, we explore potential bias in sentiment analysis tools in the context of Bengali communities that have experienced and continue to experience the impacts of colonialism. Drawing on identity categories most impacted by colonialism amongst local Bengali communities, we focused our analytic attention on gender, religion, and nationality. We conducted an algorithmic audit of all sentiment analysis tools for Bengali, available on the Python package index (PyPI) and GitHub. Despite similar semantic content and structure, our analyses showed that in addition to inconsistencies in output from different tools, Bengali sentiment analysis tools exhibit bias between different identity categories and respond differently to different ways of identity expression. Connecting our findings with colonially shaped sociocultural structures of Bengali communities, we discuss the implications of downstream bias of sentiment analysis tools.