Machine Learning Approach to Multiclass Sentiment Analysis in Dravidian Language Code-Mixed Text
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1280Keywords:
Machine Learning, Multiclass Sentiment AnalysisAbstract
In this study, we focused on sentiment analysis of code-mixed text in Dravidian languages, specifically Tamil-English and Tulu-English, using advanced natural language processing (NLP) techniques. We address the challenges posed by linguistic diversity, the unique nature of code-mixing, and the limited availability of annotated datasets with the application of the Logistic Regression machine learning model enhanced by feature engineering. Our preprocessing approach involves normalizing input text, handling class imbalances, and applying sub word tokenization to accommodate the agglutinative and mixed nature of Dravidian languages. The model was trained for a multi-class sentiment classification task, distinguishing between the classes. Our experiment achieved an F1 score of 0.4409 on Tamil-English and 0.4509 on Tulu-English, this demonstrates the model's ability and limit to process Dravidian scripts and capture sentiments in code-mixed data on a multiclass dataset. Our methodology approach can be extended and improved to tackle other low-resource language challenges in future work. The code and dataset used are available at https://github.com/ABITCONSULT/Sentiment-Analysis-in-Dravidian-Language
Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1280
Dimensions.
Open Alex.
References
Adebanji, O. O., Gelbukh, A., Calvo, H., & Ojo, O. E. (2022). Sequential models for sentiment analysis: A comparative study. In Advances in Computational Intelligence: 21st Mexican International Conference on Artificial Intelligence (MICAI 2022), Proceedings, Part II (Lecture Notes in Computer Science). Springer.
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Language in Social Media (LSM 2011) (pp. 30–38). Association for Computational Linguistics.
Agrawal, R., Chenthil Kumar, V., Muralidharan, V., & Sharma, D. (2018). No more beating about the bush: A step towards idiom handling for Indian language NLP. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association.
Andronov, M. S. (1970). Dravidian languages. Nauka Publishing House.
Bali, K., Sharma, J., Choudhury, M., & Vyas, Y. (2014). “I am borrowing ya mixing?” An analysis of English-Hindi code mixing in Facebook. In Proceedings of the First Workshop on Computational Approaches to Code Switching (pp. 116–126). Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-3914
Barman, U., Das, A., Wagner, J., & Foster, J. (2014). Code mixing: A challenge for language identification in the language of social media. In Proceedings of the First Workshop on Computational Approaches to Code Switching (pp. 13–23). Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-3902
Blackburn, S. H. (2006). Print, folklore, and nationalism in colonial South India. Orient Blackswan.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning (ICML) (pp. 161–168). ACM. https://doi.org/10.1145/1143844.1143865
Chakravarthi, B. R. (2020). HopeEDI: A multilingual hope speech detection dataset for equality, diversity, and inclusion. In Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (pp. 41–53). Association for Computational Linguistics.
Chakravarthi, B. R., & Muralidaran, V. (2021). Findings of the shared task on hope speech detection for equality, diversity, and inclusion. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion (pp. 61–72). Association for Computational Linguistics.
Chakravarthi, B. R., Anand Kumar, M., McCrae, J. P., Premjith, B., Soman, K., & Mandl, T. (2020). Overview of the track on HASOC–offensive language identification–DravidianCodeMix. In Working Notes of the Forum for Information Retrieval Evaluation (FIRE 2020). CEUR Workshop Proceedings.
Chakravarthi, B. R., Jose, N., Suryawanshi, S., Sherly, E., & McCrae, J. P. (2020). A sentiment analysis dataset for code-mixed Malayalam-English. In Proceedings of the 1st Joint Workshop of SLTU and CCURL. European Language Resources Association.
Chakravarthi, B. R., Muralidaran, V., Priyadharshini, R., & McCrae, J. P. (2020). Corpus creation for sentiment analysis in code-mixed Tamil-English text. In Proceedings of the 1st Joint Workshop of SLTU and CCURL. European Language Resources Association.
Chakravarthi, B. R., Priyadharshini, R., Jose, N., Kumar, M. A., Mandl, T., Kumaresan, P. K., Ponnusamy, R., R. L. H., McCrae, J. P., & Sherly, E. (2021). Findings of the shared task on offensive language identification in Tamil, Malayalam, and Kannada. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages (pp. 133–145). Association for Computational Linguistics.
Chakravarthi, B. R., Priyadharshini, R., Muralidaran, V., Suryawanshi, S., Jose, N., Sherly, E., & McCrae, J. P. (2020). Overview of the track on sentiment analysis for Dravidian languages in code-mixed text. In Forum for Information Retrieval Evaluation (pp. 21–24). ACM. https://doi.org/10.1145/3441501.3441515
Chanda, A., Das, D., & Mazumdar, C. (2016). Unraveling the English-Bengali code-mixing phenomenon. In Proceedings of the Second Workshop on Computational Approaches to Code Switching (pp. 80–89). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-5810
Cieliebak, M., Deriu, J. M., Egger, D., & Uzdilli, F. (2017). A Twitter corpus and benchmark resources for German sentiment analysis. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media (pp. 45–51). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-1106
Clarke, I., & Grieve, J. (2017). Dimensions of abusive language on Twitter. In Proceedings of the First Workshop on Abusive Language Online (pp. 1–10). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-3001
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8440–8451). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.747
de Gispert, A., Iglesias, G., & Byrne, B. (2015). Fast and accurate preordering for SMT using neural networks. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1012–1017). Association for Computational Linguistics. https://doi.org/10.3115/v1/N15-1105
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers) (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
El Boukkouri, H., Ferret, O., Lavergne, T., Noji, H., Zweigenbaum, P., & Tsujii, J. (2020). CharacterBERT: Reconciling ELMo and BERT for word-level open-vocabulary representations from characters. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 6903–6915). International Committee on Computational Linguistics.
Genkin, A., Lewis, D. D., & Madigan, D. (2007). Large-scale Bayesian logistic regression for text categorization. Technometrics, 49(3), 291–304. https://doi.org/10.1198/004017007000000245
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’04) (pp. 168–177). Association for Computing Machinery. https://doi.org/10.1145/1014052.1014073
Jiang, Q., Chen, L., Xu, R., Ao, X., & Yang, M. (2019). A challenge dataset and effective models for aspect-based sentiment analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 6279–6284). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1654
Jin, S., & Pedersen, T. (2018). Duluth UROP at SemEval-2018 Task 2: Multilingual emoji prediction with ensemble learning and oversampling. In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018) (pp. 482–485). Association for Computational Linguistics. https://doi.org/10.18653/v1/S18-1077
Krippendorff, K. (1970). Estimating the reliability, systematic error and random error of interval data. Educational and Psychological Measurement, 30(1), 61–70. https://doi.org/10.1177/001316447003000105
Krishnamurti, B. (2003). The Dravidian languages. Cambridge University Press.
Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining (arXiv:1901.07291). arXiv. https://arxiv.org/abs/1901.07291
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). ALBERT: A lite BERT for self-supervised learning of language representations (arXiv:1909.11942). arXiv. https://arxiv.org/abs/1909.11942
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach (arXiv:1907.11692). arXiv. https://arxiv.org/abs/1907.11692
Mahadevan, I. (2003). Early Tamil epigraphy: From the earliest times to the sixth century AD. Harvard University Press.
Mandl, T., Modha, S., Kumar, M. A., & Chakravarthi, B. R. (2020). Overview of the HASOC track at FIRE 2020: Hate speech and offensive language identification in Tamil, Malayalam, Hindi, English, and German. In Proceedings of the Forum for Information Retrieval Evaluation (FIRE 2020) (pp. 29–32). Association for Computing Machinery. https://doi.org/10.1145/3441501.3441517
Masmoudi, A., Aridhi, N., & Hadrich Belguith, L. (2025). Pre-trained model sentiment analysis of Tunisian telecommunications operators’ comments on social media. Computación y Sistemas, 29(3), 1283–1305. https://doi.org/10.13053/CyS-29-3-5918
Mukhim, B., Maji, A. K., & Das, S. (2025). Sentiment analysis with Khasi low-resource language through generation of sentiment words using machine learning. Computación y Sistemas, 29(3), 1225–1236. https://doi.org/10.13053/CyS-29-3-5915
Nath, B., Sarkar, S., & Mukhopadhyay, S. (2025). AstraMT: Instruction-tuned few-shot Assamese–English translation with context-aware prompting and reranking. Computación y Sistemas, 29(3), 1167–1178. https://doi.org/10.13053/CyS-29-3-5886
Ojo, O. E., Gelbukh, A., Calvo, H., & Adebanji, O. O. (2021). Performance study of n-grams in the analysis of sentiments. Journal of the Nigerian Society of Physical Sciences, 3(4), 477–483. https://doi.org/10.46481/jnsps.2021.201
Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04) (pp. 271–278). Association for Computational Linguistics. https://doi.org/10.3115/1218955.1218990
Park, H. (2013). An introduction to logistic regression: From basic concepts to interpretation with particular attention to nursing domain. Journal of Korean Academy of Nursing, 43(2), 154–164.
Patwa, P., Aguilar, G., Kar, S., Pandey, S., Pykl, S., Gambäck, B., Chakraborty, T., Solorio, T., & Das, A. (2020). SemEval-2020 Task 9: Overview of sentiment analysis of code-mixed tweets. In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020). Association for Computational Linguistics.
Pratapa, A., Bhat, G., Choudhury, M., Sitaram, S., Dandapat, S., & Bali, K. (2018). Language modeling for code-mixing: The role of linguistic theory-based synthetic data. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1543–1553). Association for Computational Linguistics. https://doi.org/10.18653/v1/P18-1143
Reyes-Cocoletzi, L., Aldama-Ramos, J. A., Elias-Zapata, A., Betancourt-González, J., & Rojas-Hernández, J. (2025). Detection of tendency to depression through text analysis. Computación y Sistemas, 29(3). https://doi.org/10.13053/CyS-29-3-5887
Sakuntharaj, R., & Mahesan, S. (2016). A novel hybrid approach to detect and correct spelling in Tamil text. In 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS) (pp. 1–6). IEEE.
Sakuntharaj, R., & Mahesan, S. (2017). Use of a novel hash-table for speeding-up suggestions for misspelt Tamil words. In 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1–5). IEEE.
Salomon, R. (1998). Indian epigraphy: A guide to the study of inscriptions in Sanskrit, Prakrit, and the other Indo-Aryan languages. Oxford University Press.
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter (arXiv:1910.01108). arXiv. https://arxiv.org/abs/1910.01108
Sekhar, A. C. (1951). Evolution of Malayalam. Bulletin of the Deccan College Research Institute, 12(1–2), 1–216.
Severyn, A., Moschitti, A., Uryupina, O., Plank, B., & Filippova, K. (2014). Opinion mining on YouTube. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1252–1261). Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-1118
Shah, K., Patel, H., Sanghvi, D., & Shah, M. (2020). A comparative analysis of logistic regression, random forest, and KNN models for text classification. Augmented Human Research, 5(1), 1–16.
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., & Chanona-Hernández, L. (2013). Empirical study of machine learning based approach for opinion mining in tweets. In Advances in Artificial Intelligence (MICAI 2012) (Lecture Notes in Computer Science, Vol. 7629, pp. 1–14). Springer. https://doi.org/10.1007/978-3-642-37807-2_1
Sowmya Lakshmi, B. S., & Shambhavi, B. R. (2017). An automatic language identification system for code-mixed English-Kannada social media text. In 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (pp. 1–5). IEEE. https://doi.org/10.1109/CSITSS.2017.8447784
Sridhar, S. N. (1978). On the functions of code-mixing in Kannada. International Journal of the Sociology of Language, 16, 109–118.
Sridhar, S. N., & Sridhar, K. K. (1980). The syntax and psycholinguistics of bilingual code mixing. Canadian Journal of Psychology, 34(4), 407–416.
Takahashi, T. (1995). Tamil love poetry and poetics (Vol. 9). Brill.
Thamburaj, K. P., & Rengganathan, V. (2015). A critical study of SPM Tamil literature exam paper. Asian Journal of Assessment in Teaching and Learning, 5, 13–24.
Thamburaj, K. P., Arumugum, L., & Samuel, S. J. (2015). An analysis on keyboard writing skills in online learning. In 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET) (pp. 373–377). IEEE.
Thavareesan, S., & Mahesan, S. (2019). Sentiment analysis in Tamil texts: A study on machine learning techniques and feature representation. In 2019 14th Conference on Industrial and Information Systems (ICIIS) (pp. 320–325). IEEE. https://doi.org/10.1109/ICIIS47346.2019.9063341
Thavareesan, S., & Mahesan, S. (2020a). Sentiment lexicon expansion using Word2Vec and fastText for sentiment prediction in Tamil texts. In 2020 Moratuwa Engineering Research Conference (MERCon) (pp. 272–276). IEEE. https://doi.org/10.1109/MERCon50084.2020.9185369
Thavareesan, S., & Mahesan, S. (2020b). Word embedding-based part of speech tagging in Tamil texts. In 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) (pp. 478–482). IEEE. https://doi.org/10.1109/ICIIS51140.2020.9342640
Thenmozhi, D., & Aravindan, C. (2018). Ontology-based Tamil-English cross-lingual information retrieval system. Sādhanā, 43(10).
Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., & Sun, C. (2017). Facebook sentiment: Reactions and emojis. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media (pp. 11–16). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-1102
Vyas, Y., Gella, S., Sharma, J., Bali, K., & Choudhury, M. (2014). POS tagging of English-Hindi code-mixed social media content. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 974–979). Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1105
Wiebe, J., Wilson, T., & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2), 165–210. https://doi.org/10.1007/s10579-005-7880-9
Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (pp. 347–354). Association for Computational Linguistics.
Winata, G. I., Lin, Z., & Fung, P. (2019). Learning multilingual meta-embeddings for code-switching named entity recognition. In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP 2019) (pp. 181–186). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4320
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019). Predicting the type and target of offensive posts in social media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1) (pp. 1415–1420). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1144
Zampieri, M., Nakov, P., Rosenthal, S., Atanasova, P., Karadzhov, G., Mubarak, H., Derczynski, L., Pitenis, Z., & Çöltekin, Ç. (2020). SemEval-2020 Task 12: Multilingual offensive language identification in social media (OffensEval 2020). In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020). Association for Computational Linguistics.
Zvelebil, K. V. (1991). Comments on the Tolkappiyam theory of literature. Archív Orientální, 59, 345–359.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
