Daniel D. O’Connell


2023

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SADTech@DravidianLangTech: Multimodal Sentiment Analysis of Tamil and Malayalam
Abhinav Patil | Sam Briggs | Tara Wueger | Daniel D. O’Connell
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

We present several models for sentiment analysis of multimodal movie reviews in Tamil and Malayalam into 5 separate classes: highly negative, negative, neutral, positive, and highly positive, based on the shared task, “Multimodal Abusive Language Detection and Sentiment Analysis” at RANLP-2023. We use transformer language models to build text and audio embeddings and then compare the performance of multiple classifier models trained on these embeddings: a Multinomial Naive Bayes baseline, a Logistic Regression, a Random Forest, and an SVM. To account for class imbalance, we use both naive resampling and SMOTE. We found that without resampling, the baseline models have the same performance as a naive Majority Class Classifier. However, with resampling, logistic regression and random forest both demonstrate gains over the baseline.