@inproceedings{barua-etal-2025-cuet-novice,
    title = "{CUET}{\_}{N}ovice@{D}ravidian{L}ang{T}ech 2025: A {B}i-{GRU} Approach for Multiclass Political Sentiment Analysis of {T}amil {T}witter ({X}) Comments",
    author = "Barua, Arupa  and
      Osama, Md  and
      Dey, Ashim",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.dravidianlangtech-1.87/",
    pages = "496--501",
    ISBN = "979-8-89176-228-2",
    abstract = "Political sentiment analysis in multilingual content poses significant challenges in capturing the subtle variations of diverse sentiments expressed in complex and low-resourced languages. Accurately classifying sentiments, whether positive, negative, or neutral, is crucialfor understanding public discourse. A shared task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments, organized by DravidianLangTech@NAACL 2025, provided an opportunity to tackle these challenges. For this task, we implemented two data augmentation techniques, which are synonym replacement and back translation, and then explored various machine learning (ML) algorithms, including Logistic Regression, Decision Tree, Random Forest, SVM, and MultiNomial Naive Bayes. To capture the semantic meanings more efficiently, we experimented with deep learning (DL) models, including GRU, BiLSTM, BiGRU, and a hybrid CNN-BiLSTM.The Bidirectional Gated Recurrent Unit (BiGRU) achieved the best macro-F1 (MF1) score of 0.33, securing the 17th position in the sharedtask. These findings underscore the challenges of political sentiment analysis in low-resource languages and the need for advanced language-specific models for improved classification."
}