Kavin Vishnu false


2023

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KEC_AI_NLP@DravidianLangTech: Sentiment Analysis in Code Mixture Language
Kogilavani Shanmugavadivel | Malliga Subaramanian | VetriVendhan S | Pramoth Kumar M | Karthickeyan S | Kavin Vishnu N
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

Sentiment Analysis is a process that involves analyzing digital text to determine the emo- tional tone, such as positive, negative, neu- tral, or unknown. Sentiment Analysis of code- mixed languages presents challenges in natural language processing due to the complexity of code-mixed data, which combines vocabulary and grammar from multiple languages and cre- ates unique structures. The scarcity of anno- tated data and the unstructured nature of code- mixed data are major challenges. To address these challenges, we explored various tech- niques, including Machine Learning models such as Decision Trees, Random Forests, Lo- gistic Regression, and Gaussian Na ̈ıve Bayes, Deep Learning model, such as Long Short- Term Memory (LSTM), and Transfer Learning model like BERT, were also utilized. In this work, we obtained the dataset from the Dravid- ianLangTech shared task by participating in a competition and accessing train, development and test data for Tamil Language. The results demonstrated promising performance in senti- ment analysis of code-mixed text. Among all the models, deep learning model LSTM pro- vides best accuracy of 0.61 for Tamil language.