Sentiment Analysis at Document level of Telugu Data from Multi-Domains

Finding common threads of optimism or negative in text is the goal of sentiment analysis. In the business world, it is used to learn about a product's reception, a customer's identity, and their expectations of a firm by monitoring the tone of online conversations on forums like Reddit and Twitter. Sentiment analysis in various languages has arisen as a separate topic of NLP as businesses seek for client feedback in previously untapped markets. The stakes could not be greater, since Telugu is a Dravidian language spoken by almost 82 million people. Little effort, such as annotated data and software, is put towards supporting Telugu, hence the language is often overlooked. To better understand the sentiment of the data, we conducted our research using the "Sentirama" dataset and, as you'll see in the paper, we used a variety of Machine Learning models (including SVM-linear, SVM-quadratic, SVM-polynomial, Random Forest, Naive Bayes, and KNN) and Featured concepts (including word2vec+ (CBOW or skip-gram), TF-IDF, and Fastext). To find the best Deep-learning model for Telugu sentiment analysis, we also tried out LSTM, Bidirectional-LSTM, and 1D-CNN.