Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME
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arXiv
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| Format: | Preprint |
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2025
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| author | Paul, Bidyarthi Rahman, SM Musfiqur Biswas, Dipta Hasan, Md. Ziaul Hossain, Md. Zahid |
| author_facet | Paul, Bidyarthi Rahman, SM Musfiqur Biswas, Dipta Hasan, Md. Ziaul Hossain, Md. Zahid |
| contents | Research on understanding emotions in written language continues to expand, especially for understudied languages with distinctive regional expressions and cultural features, such as Bangla. This study examines emotion analysis using 22,698 social media comments from the EmoNoBa dataset. For language analysis, we employ machine learning models: Linear SVM, KNN, and Random Forest with n-gram data from a TF-IDF vectorizer. We additionally investigated how PCA affects the reduction of dimensionality. Moreover, we utilized a BiLSTM model and AdaBoost to improve decision trees. To make our machine learning models easier to understand, we used LIME to explain the predictions of the AdaBoost classifier, which uses decision trees. With the goal of advancing sentiment analysis in languages with limited resources, our work examines various techniques to find efficient techniques for emotion identification in Bangla. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME Paul, Bidyarthi Rahman, SM Musfiqur Biswas, Dipta Hasan, Md. Ziaul Hossain, Md. Zahid Computation and Language Machine Learning Research on understanding emotions in written language continues to expand, especially for understudied languages with distinctive regional expressions and cultural features, such as Bangla. This study examines emotion analysis using 22,698 social media comments from the EmoNoBa dataset. For language analysis, we employ machine learning models: Linear SVM, KNN, and Random Forest with n-gram data from a TF-IDF vectorizer. We additionally investigated how PCA affects the reduction of dimensionality. Moreover, we utilized a BiLSTM model and AdaBoost to improve decision trees. To make our machine learning models easier to understand, we used LIME to explain the predictions of the AdaBoost classifier, which uses decision trees. With the goal of advancing sentiment analysis in languages with limited resources, our work examines various techniques to find efficient techniques for emotion identification in Bangla. |
| title | Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2506.10154 |