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@article{182456,
        author = {Om Tulsani and Prashant Kulkarni and Shubhangi Tidake},
        title = {Twitter Sentiment Analysis using NLP: A Data-Driven Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1892-1895},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182456},
        abstract = {In the age of digital communication, social media platforms like Twitter have evolved into rich sources of real-time public opinion and sentiment. This study presents a systematic approach to sentiment analysis of Twitter data using Natural Language Processing (NLP). The research aims to classify tweets into positive and negative sentiments through the development of a sentiment classifier trained on preprocessed Twitter data. The methodology spans data acquisition, preprocessing, visualization via word clouds, model training using deep learning techniques, and final deployment strategies. The findings demonstrate that NLP-based sentiment analysis can effectively interpret public sentiment, offering insights for applications in marketing, politics, and public opinion mining. The developed model achieved 94% accuracy on test data, demonstrating high effectiveness in binary sentiment classification.},
        keywords = {Deep Learning, Natural Language Processing, Sentiment Analysis, Text Classification, Twitter.},
        month = {July},
        }