A Sequential DNN for Sentiment Analysis of Dravidian Code-Mixed Language Comments on YouTube.

Abstract

A method for determining if a block of text is positive, neutral, or negative is sentiment analysis. As code-mixed material in many native languages is becoming increasingly widespread, there is also an increasing need for intense research in order to produce satisfactory results. This research paper aims to classify the sentiments from a data set of comments/posts into pre-defined classes belonging to the code-mixed text in Tamil, Malayalam, and Kannada by utilizing the Sequential Deep Learning model on the code-mixed data set. The sequential model achieved an f1-score of 0.20 for Tamil-English, 0.48 for Malayalam-English, and 0.47 for Kannada-English data sets. The results were submitted to the competition ‘Shared Task on Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages’ organized b y DravidianLangTech.