Abstract
Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages.
In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.
N. Choudhary and R. Singh—These authors have contributed equally to this work.
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Choudhary, N., Singh, R., Bindlish, I., Shrivastava, M. (2023). Sentiment Analysis of Code-Mixed Languages Leveraging Resource Rich Languages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13397. Springer, Cham. https://doi.org/10.1007/978-3-031-23804-8_9
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