IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1259–1264 Barcelona, Spain (Online), December 12, 2020. 1259 IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection Vivek Srivastava, Mayank Singh Indian Institute of Technology Gandhinagar Gujarat India {vivek.srivastava, singh.mayank}@iitgn.ac.in Abstract Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech. It is a frequently used pattern of communication on various platforms such as social media sites, online gaming, product reviews, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed approach shows an improvement in the system performance as compared to the Bi-LSTM based neural classifier. The results present an opportunity to understand various other nuances of code-mixing in the textual data, such as humor-detection, intent classification, etc. 1 Introduction Code mixing is one of the most frequent styles of communication in multilingual communities, such as India. This pattern of communication on various platforms such as social media, online gaming, online product reviews, etc. makes it difficult to understand the sentiment of the text. Sentiment classification of the code-mixed text is useful in the scenarios of socially or politically driven discussions, fake news propagation, etc. Some of the major challenges with the text in the code-mixed language are: • Ambiguity in language identification: is, me, to are some examples of the words that are ambiguous to classify as English and Hindi without proper knowledge of context. • Spelling variations: E.g., jaldi, jldi, jldiii,.. are some variations for the word hurry in English. • Misplaced/ skipped punctuation: E.g., Aap kb se cricket khelne lage..never saw u bfr. The sentence in the example misses a question mark(?) apart from other necessary modifications to make the structure of the sentence correct. • Missing context: E.g., Note kr lijiye.. Bandi chal rahi h ;) is a code-mixed sentence and demonetisa- tion (notebandi) is the hidden context. With the increasing popularity of using code-mixing on social media platforms, the interest to study the various dynamics of code-mixing is seeking a boom. Multiple works on language identification (Barman et al., 2014; Das and Gamb¨ack, 2014), POS tagging (Vyas et al., 2014; Ghosh et al., 2016), named entity recognition (Singh et al., 2018a; Singh et al., 2018b), etc. shows the challenges and the opportunities with the code-mixed data. Pang et al. (2008) presents a survey of the approaches to understand the opinions and sentiments on various platforms. Dos Santos and Gatti (2014) performs the sentiment analysis task of the short text messages on two corpora from different domains and present their findings. Kouloumpis et al. (2011) presents multiple experiments to understand the sentiment of Twitter messages using …

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