B n S ent M ix: A Diverse B engali- E nglish Code-Mixed Dataset for Sentiment Analysis (2025)

Abstract: The widespread availability of code-mixed data in digital spaces can provide valuable insights into low-resource languages like Bengali, which have limited annotated corpora. Sentiment analysis, a pivotal text classification task, has been explored across multiple languages, yet code-mixed Bengali remains underrepresented with no large-scale, diverse benchmark. Code-mixed text is particularly challenging as it requires the understanding of multiple languages and their interaction in the same text. We address this limitation by introducing BnSentMix, a sentiment analysis dataset on code-mixed Bengali comprising 20,000 samples with 4 sentiment labels, sourced from Facebook, YouTube, and e-commerce sites. By aggregating multiple sources, we ensure linguistic diversity reflecting realistic code-mixed scenarios. We implement a novel automated text filtering pipeline using fine-tuned language models to detect code-mixed samples and expand code-mixed text corpora. We further propose baselines using machine learning, neural networks, and transformer-based language models. The availability of a diverse dataset is a critical step towards democratizing NLP and ultimately contributing to a better understanding of code-mixed languages.

## introduction in the rapidly evolving digital landscape, codemixing has become increasingly prevalent, particularly in multilingual societies. code-mixing is the phenomenon of alternating between two or more languages within a single conversation or sentence @xcite . codemixing can occur in various forms, including intrasentential switching, where words from different languages appear within the same sentence, and intra-word switching, where elements from other languages combine to form a single word @xcite . bengali-english: movietar first half bhalo laage nai. translation: i did not like the movie's first half. ## bnsentmix dataset the bnsentmix data has been collected from multiple data sources to reflect realistic code-mixed texts commonly found in digital spaces. we labeled the data using four distinct sentiments: the commonly used positive, negative, and neutral sentiments, as well as a mixed sentiment. as illustrated in fig. 1 , the mixed sentiment represents instances where both positive and negative sentiments are conveyed within different parts of the text. we decided to include the mixed label because the associated sentences are frequently observed in everyday texts and cannot be correctly classified under the traditional sentiment labels. ## conclusion we introduce bnsentmix, a novel sentiment analysis dataset tailored for code-mixed bengali-english. our work opens several potential research avenues for code-mixed bengali. researchers can explore other tasks, such as hate speech, offensive language, and abusive content detection on codemixed data. our work addresses a significant gap for low-resource languages and sets a new standard for sentiment analysis in code-mixed bengali-english.