Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

Parita Shah (Department of Computer Engineering, Sarva Vidyalaya Kelavani Mandal managed Vidush Somany Institute of Technology and Research, Kadi, India)
Priya Swaminarayan (Faculty of Information Technology and Computer Science, Parul University, Vadodara, India)
Maitri Patel (Department of Computer Engineering, Gandhinagar University, India)

Journal of ICT Research and Applications
Vol. 17 No. 1 (2023)

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.