@inproceedings{patil-etal-2023-sadtech,
title = "{SADT}ech@{D}ravidian{L}ang{T}ech: Multimodal Sentiment Analysis of {T}amil and {M}alayalam",
author = "Patil, Abhinav and
Briggs, Sam and
Wueger, Tara and
O{'}Connell, Daniel D.",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.37",
pages = "250--257",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SADTech@DravidianLangTech: Multimodal Sentiment Analysis of Tamil and Malayalam
%A Patil, Abhinav
%A Briggs, Sam
%A Wueger, Tara
%A O’Connell, Daniel D.
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F patil-etal-2023-sadtech
%X 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.
%U https://aclanthology.org/2023.dravidianlangtech-1.37
%P 250-257
Markdown (Informal)
[SADTech@DravidianLangTech: Multimodal Sentiment Analysis of Tamil and Malayalam](https://aclanthology.org/2023.dravidianlangtech-1.37) (Patil et al., DravidianLangTech-WS 2023)
ACL