Gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1247–1252 Barcelona, Spain (Online), December 12, 2020. 1247 gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data Sunil Gundapu Language Technologies Research Centre KCIS, IIIT Hyderabad Telangana, India sunil.g@research.iiit.ac.in Radhika Mamidi Language Technologies Research Centre KCIS, IIIT Hyderabad Telangana, India radhika.mamidi@iiit.ac.in Abstract The phenomenon of mixing the vocabulary and syntax of multiple languages within the same utterance is called Code-Mixing. This is more evident in multilingual societies. In this paper, we have developed a system for SemEval 2020: Task 9 on Sentiment Analysis for Code-Mixed Social Media Text. Our system first generates two types of embeddings for the social media text. In those, the first one is character level embeddings to encode the character level information and to handle the out-of-vocabulary entries and the second one is FastText word embeddings for capturing morphology and semantics. These two embeddings were passed to the LSTM network and the system outperformed the baseline model. 1 Introduction Code-Mixing is a phenomenon which is evident in multilingual societies (Shana Poplack et al., 2003). It reflects the use of distinct grammatical systems and vocabulary of the languages being used simultaneously in a single utterance or conversation. This technique used in communication commonly is widely found today in popular social media platforms like Twitter, Facebook, Instagram in the form of posts, comments, replies, especially in chats. This is evident in multilingual societies like India, Canada, Ireland, South Africa, Switzerland, and many others. India has officially recognized 22 regional languages1. So, in multilingual societies like India, most of the social media users predominantly integrate the well-known language, like English, with their native languages. 560 million Internet users2 in India exchange information by mixing their regional languages with prominent language like English, which produces a huge amount of code-mixed social media corpus. One such trending combination is the mixing of Hindi and English with the output in Hinglish3 (Hi-En) code-mixed data. Consider the example sentence which illustrates the code-mixing phenomenon being addressed in this paper. “Congratulations/Eng Sir/Eng Ji/Hin Dobara/Hin PM/Eng banee/Hin ki/Hin hardik/Hin subhkamnaye/Hin aapko/Hin ./O”. (Translation into English: Congratulations sir, Best wishes to become Prime Minister again). The words followed by language tags /Hin, /Eng, and /O correspond to Hindi, English and Other respectively. Sentiment analysis of code-mixed data has become a prominent research area in recent times in the field of NLP. But identifying the sentiment in code-mixed data is hard since it poses the following challenges: (i) Romanized code-mixed data is noisy and ambiguous in nature. (ii) Accessible datasets are smaller in size to tune neural networks. (iii) Frequent occurence of non-standard spellings (such as pyaaarr, goooood). (iv) The phrase/word contractions (cmng for coming, IDK for I don’t know). (v) Spelling variations. A single word pyaar (love), can be written as “piyar”, “pyaarrrr”, “peyar”, “pyar”, or “piyaar”, etc. To handle these challenges we postulate that FastText embeddings enrich the word vectors with This work is licensed under a Creative Commons …

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