Abstract
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.- Anthology ID:
- 2023.dravidianlangtech-1.37
- Volume:
- Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Bharathi R. Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Sajeetha Thavareesan, Elizabeth Sherly
- Venues:
- DravidianLangTech | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 250–257
- Language:
- URL:
- https://aclanthology.org/2023.dravidianlangtech-1.37
- DOI:
- Cite (ACL):
- Abhinav Patil, Sam Briggs, Tara Wueger, and Daniel D. O’Connell. 2023. SADTech@DravidianLangTech: Multimodal Sentiment Analysis of Tamil and Malayalam. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 250–257, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- SADTech@DravidianLangTech: Multimodal Sentiment Analysis of Tamil and Malayalam (Patil et al., DravidianLangTech-WS 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.dravidianlangtech-1.37.pdf