Abstract
Social media has been experiencing an enormous amount of activity from millions of people across the globe over last few years. This resulted in the accumulation of substantial amount of textual data and increased several opportunities of analysis. Sentiment analysis and classification is one such task where the opinion expressed in the text is identified and classified accordingly. This becomes even more trickier in code-mixed text due to free style of writing which does not have a proper syntactic structure. In this paper, we worked on such Hind–English code-mixed texts obtained from SentiMix shared task of SemEval-2020. We created a novel customized embedding model for feature generation from Hindi–English code-mixed texts to classify them to various sentiments like positive, neutral and negative using deep learning techniques. It is observed that attention-based CNN-Bi-LSTM model has achieved better performance out of all models with 70.32% F1-score.
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Tulasi Sasidhar, T., Premjith, B., Sreelakshmi, K., Soman, K.P. (2021). Sentiment Analysis on Hindi–English Code-Mixed Social Media Text. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-33-4543-0_65
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DOI: https://doi.org/10.1007/978-981-33-4543-0_65
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