%0 Conference Proceedings %T Sentiment Analysis of Dravidian Code Mixed Data %A Mandalam, Asrita Venkata %A Sharma, Yashvardhan %Y Chakravarthi, Bharathi Raja %Y Priyadharshini, Ruba %Y Kumar M, Anand %Y Krishnamurthy, Parameswari %Y Sherly, Elizabeth %S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages %D 2021 %8 April %I Association for Computational Linguistics %C Kyiv %F mandalam-sharma-2021-sentiment %X This paper presents the methodologies implemented while classifying Dravidian code-mixed comments according to their polarity. With datasets of code-mixed Tamil and Malayalam available, three methods are proposed - a sub-word level model, a word embedding based model and a machine learning based architecture. The sub-word and word embedding based models utilized Long Short Term Memory (LSTM) network along with language-specific preprocessing while the machine learning model used term frequency–inverse document frequency (TF-IDF) vectorization along with a Logistic Regression model. The sub-word level model was submitted to the the track ‘Sentiment Analysis for Dravidian Languages in Code-Mixed Text’ proposed by Forum of Information Retrieval Evaluation in 2020 (FIRE 2020). Although it received a rank of 5 and 12 for the Tamil and Malayalam tasks respectively in the FIRE 2020 track, this paper improves upon the results by a margin to attain final weighted F1-scores of 0.65 for the Tamil task and 0.68 for the Malayalam task. The former score is equivalent to that attained by the highest ranked team of the Tamil track. %U https://aclanthology.org/2021.dravidianlangtech-1.6 %P 46-54