JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1276–1280 Barcelona, Spain (Online), December 12, 2020. 1276 JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation Avishek Garain1, Sainik Kumar Mahata2, Dipankar Das3 123Jadavpur University, Kolkata 1avishekgarain@gmail.com, 2sainik.mahata@gmail.com, 3dipankar.dipnil2005@gmail.com Abstract Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual peo- ple, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we fo- cus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was ”sainik.mahata” and team name was ”JUNLP”. We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2% when tested using metrics prepared by the organizers of the task. 1 Introduction India has a linguistically diverse population due to its long history of foreign acquaintances. English, one of those borrowed languages, became an integral part of the education system and hence gave rise to a population who are very comfortable using bilingualism in their day to day communication. Due to such language diversity and dialects, frequent code-mixing is encountered during conversations. Further, due to the emergence of social media, the practice has become even more widespread. The phenomenon is so common that it is often considered as a different (emerging) variety of the language, e.g., Benglish (Bengali-English) and Hinglish (Hindi-English). This phenomenon poses a great challenge to the existing domains of Natural Language Processing (NLP) such as Sentiment Analysis as primarily the language technologies, such as parsing, Parts-of- Speech (POS) tagging, etc., are built for English. Furthermore, labeled/annotated data of such category are hard to come by and hence leads to misfiring when using straight-forward machine learning algo- rithms. In this work, we participated in SemEval-2020 Sentimix Task1 and attempted to solve the chore of sentiment analysis of English-Hindi code-mixed sentences. Initially, our approach includes the use of feature extraction algorithms on the data, procured by the organizers. Thereafter, we used Support Vector Regression coupled with Grid Search algorithm to clas- sify the code-mixed sentences to its respective sentiment class. This approach, when tested using the metrics prepared by the organizers, returned an f1-score of 66.2%. The rest of the paper is organized as follows. Section 2 briefly the quantifies the English-Hindi code- mixed data procured by the organizers of the task. Section 3 provides a descriptive literature of our proposed approach. This will be followed by the results and concluding remarks in Section 4 and 5. This work …

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