Telugu Movie Review Classification using Machine Learning Techniques

  • Srinivasu Badugu, G V Subba Raju


In this trendy age of conversion, everybody uses on-line services for numerous daily activities wish to realize product practicality or a couple of piece of movie-related info by varied blogs. This short description of a film is nothing however the review of the film, which supplies the opinion regarding the film by many, authors/authenticated persons. These reviews  terribly helpful to enhance and verify the gain and losses  of the film. In the course of this research we obtained Telugu language film reviews. We use three human annotators for annotation 2994 review sentences. we have a tendency to apply to pre-process (Sentence segmentation, Tokenization). We prefer to  classify film reviews using 3 machine learning algorithms during this work. The algorithms are Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression(LR). we have a tendency to train and check our models using 3 completely different sizes like 80%, 70%, and 60% for training and 20%, 30%, 40% testing. We consider SVM to be extremely accurate (78.13 percent) in 20% test dataset. With 2994 reviews for training, of the three algorithms, SVM had the best accuracy of 87.4%.

How to Cite
Srinivasu Badugu, G V Subba Raju. (2020). Telugu Movie Review Classification using Machine Learning Techniques. International Journal of Advanced Science and Technology, 29(05), 9511-9519. Retrieved from