This study presents a novel and comprehensive study on sentiment analysis for Kokborok, a low-resource Tibeto-Burman language primarily spoken in the Indian state of Tripura. The lack of annotated corpora and linguistic tools has significantly impeded the development of Natural Language Processing (NLP) applications for Kokborok. Addressing this challenge, we developed a manually verified and annotated dataset consisting of 7,521 Kokborok sentences, each labeled as expressing a positive, negative, or neutral sentiment. To analyze the sentiment (in terms of three classes: Positive, Negative and Neutral), we implemented and evaluated four traditional machine learning models: Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes. Given the class imbalance in the dataset, we further employed feature extraction techniques such as Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) in separate pipelines, both with and without the application of over-sampling methods. Thereafter, performing the comparative analysis, it was found that when incorporating oversampling with the BoW feature extraction technique and the Logistic Regression model, we derived the highest accuracy of 100% in training and 90% in testing. Similarly, with the TF-IDF feature extraction technique, we derived the highest accuracy of 100% in training and 89% in testing, with the Random Forest and SVM models. However, without the incorporation of oversampling for the extraction techniques employed, the accuracy and other evaluation metrics derived very poor results. Hence, making the feature extraction pipeline Logistic regression along with BoW and oversampling to be an appropriate choice for this study.
