@inproceedings{mustakim-etal-2022-cuet,
title = "{CUET}-{NLP}@{D}ravidian{L}ang{T}ech-{ACL}2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews",
author = "Mustakim, Nasehatul and
Jannat, Nusratul and
Hasan, Md and
Hossain, Eftekhar and
Sharif, Omar and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.30/",
doi = "10.18653/v1/2022.dravidianlangtech-1.30",
pages = "191--198",
abstract = "With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people`s opinions on diverse issues. Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advancement of the multimodality of the content users on social networking sites. This paper presents a technique for classifying multimodal sentiment using the text modality into five categories: highly positive, positive, neutral, negative, and highly negative categories. A shared task was organized to develop models that can identify the sentiments expressed by the videos of movie reviewers in both Malayalam and Tamil languages. This work applied several machine learning techniques (LR, DT, MNB, SVM) and deep learning (BiLSTM, CNN+BiLSTM) to accomplish the task. Results demonstrate that the proposed model with the decision tree (DT) outperformed the other methods and won the competition by acquiring the highest macro $f_1$-score of 0.24."
}
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<abstract>With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people‘s opinions on diverse issues. Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advancement of the multimodality of the content users on social networking sites. This paper presents a technique for classifying multimodal sentiment using the text modality into five categories: highly positive, positive, neutral, negative, and highly negative categories. A shared task was organized to develop models that can identify the sentiments expressed by the videos of movie reviewers in both Malayalam and Tamil languages. This work applied several machine learning techniques (LR, DT, MNB, SVM) and deep learning (BiLSTM, CNN+BiLSTM) to accomplish the task. Results demonstrate that the proposed model with the decision tree (DT) outperformed the other methods and won the competition by acquiring the highest macro f₁-score of 0.24.</abstract>
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%0 Conference Proceedings
%T CUET-NLP@DravidianLangTech-ACL2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews
%A Mustakim, Nasehatul
%A Jannat, Nusratul
%A Hasan, Md
%A Hossain, Eftekhar
%A Sharif, Omar
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mustakim-etal-2022-cuet
%X With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people‘s opinions on diverse issues. Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advancement of the multimodality of the content users on social networking sites. This paper presents a technique for classifying multimodal sentiment using the text modality into five categories: highly positive, positive, neutral, negative, and highly negative categories. A shared task was organized to develop models that can identify the sentiments expressed by the videos of movie reviewers in both Malayalam and Tamil languages. This work applied several machine learning techniques (LR, DT, MNB, SVM) and deep learning (BiLSTM, CNN+BiLSTM) to accomplish the task. Results demonstrate that the proposed model with the decision tree (DT) outperformed the other methods and won the competition by acquiring the highest macro f₁-score of 0.24.
%R 10.18653/v1/2022.dravidianlangtech-1.30
%U https://aclanthology.org/2022.dravidianlangtech-1.30/
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.30
%P 191-198
Markdown (Informal)
[CUET-NLP@DravidianLangTech-ACL2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews](https://aclanthology.org/2022.dravidianlangtech-1.30/) (Mustakim et al., DravidianLangTech 2022)
ACL