@inproceedings{vishwakarma-kumar-2025-mnlp,
    title = "{MNLP}@{D}ravidian{L}ang{T}ech 2025: Transformers vs. Traditional Machine Learning: Analyzing Sentiment in {T}amil Social Media Posts",
    author = "Vishwakarma, Abhay  and
      Kumar, Abhinav",
    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/fix-sig-urls/2025.dravidianlangtech-1.72/",
    pages = "404--408",
    ISBN = "979-8-89176-228-2",
    abstract = "Sentiment analysis in Natural Language Processing (NLP) aims to categorize opinions in text. In the political domain, understanding public sentiment is crucial for influencing policymaking. Social media platforms like X (Twitter) provide abundant sources of real-time political discourse. This study focuses on political multiclass sentiment analysis of Tamil comments from X, classifying sentiments into seven categories: substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above. A number of traditional machine learning such as Naive Bayes, Voting Classifier (an ensemble of Decision Tree, SVM, Naive Bayes, K-Nearest Neighbors, and Logistic Regression) and deep learning models such as LSTM, deBERTa, and a hybrid approach combining deBERTa embeddings with an LSTM layer are implemented. The proposed ensemble-based voting classifier achieved best performance among all implemented models with an accuracy of 0.3750, precision of 0.3387, recall of 0.3250, and macro-F1-score of 0.3227."
}