Journal of Cognitive Human-Computer Interaction JCHCI 2771-1463 2771-1471 10.54216/JCHCI https://www.americaspg.com/journals/show/2702 2021 2021 Mizoram University, Aizawl-796004, India Kumal Kumal Mizoram University, Aizawl-796004, India Shivam Kumar This paper explores sentiment analysis in Indian languages through a deep learning approach, combining machine learning techniques with natural language processing (NLP). Three neural network architectures—CNN, LSTM, and GRU—are employed to construct sentiment analysis models. Additionally, transfer learning is utilized via FastText, MURIL, and IndicBERT embeddings. The models are trained and evaluated on a translated dataset derived from the Sentiment140 dataset from Kaggle. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models. The study addresses the challenges of sentiment analysis in Indian languages by leveraging deep learning techniques and linguistic diversity, providing insights into sentiment analysis across diverse languages and cultures. Furthermore, this project extends its analysis to include work on Gujarati, Marathi, and Sindhi languages, contributing to the understanding of sentiment analysis in a broader spectrum of Indian languages 2024 2024 14 21 10.54216/JCHCI.080102 https://www.americaspg.com/articleinfo/25/show/2702