Sentiment Analysis Design and Development for Low Resource Languages in the Case of Telugu

The use of sentiment analysis has become more widespread because it is necessary to filter and analyze information on the internet. It has a wide range of applications, including monitoring social media market research and opinion mining. Still, this development is restricted to few languages with enough resources. The Telugu language lags behind in this field of study, even though it is the fourth most spoken language in India and generates a vast quantity of data every day. In this research paper, we develop a trustworthy source for sentiment analysis in Telugu. To use in sentiment analysis, the data is annotated with Telugu movie reviews. We extracted 1844 sentences from 100 film reviews. We annotated the data with two annotators and calculated the kappa coefficient to determine the annotators' inter-rater reliability. We obtained a kappa value of 0.90 for 1844 sentences, indicating nearly perfect agreement. After the annotators' disagreements and discrepancies were resolved, 1807 sentences were chosen. For feature extraction, we used two vectorization methods: TF-IDF and Count vectorization. Using the two vectorization methods, we used SVM and Logistic regression. We used two vectorization approaches to test different split ratios such as 80-20%, 70-30%, and 60-40% on SVM and Logistic regression. The outcomes of the various combinations are compared. We discovered that combining TF-IDF with SVM for a 70-30% ratio yields the highest accuracy among the combinations tested on our dataset.