@inproceedings{nazeem-etal-2024-enhancing,
    title = "Enhancing Trust and Interpretability in {M}alayalam Sentiment Analysis with Explainable {AI}",
    author = "R, Anitha  and
      R R, Rajeev  and
      Nazeem, Meharuniza  and
      S, Navaneeth",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.icon-1.12/",
    pages = "102--108",
    abstract = "Natural language processing (NLP) has seen a rise in the use of explainable AI, especially for low-resource languages like Malayalam. This study builds on our earlier research on sentiment analysis which uses identified views to classify and understand the context. Support Vector Machine (SVM) and Random Forest (RF) classifiers are two machine learning approaches that we used to do sentiment analysis on the Kerala political opinion corpus. Using Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) features, we construct feature vectors for sentiment analysis. In this, analysis of the Random Forest classifier{'}s performance shows that it outperforms SVM in terms of accuracy and efficiency, with an accuracy of 85.07 {\%}. Using Local Interpretable Model-Agnostic Explanations (LIME) as a foundation, we address the interpretability of text classification and sentiment analysis models. This integration increases user confidence and model use by offering concise and understandable justifications for model predictions. The study lays the groundwork for future developments in the area by demonstrating the significance of explainable AI in NLP for low-resource languages."
}