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@article{161238,
        author = {SAMRUDDHI MANOJ MAHALLE and DR V. S. GULHANE and DR AVINASH P. JADHAO},
        title = {Result Analysis on Twitter Sentiment Analysis using BERT Algorithm },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {3},
        pages = {11-19},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161238},
        abstract = {With the exponential growth of web technology and the increasing popularity of social networking sites like Twitter, there has been an unprecedented volume of user-generated data available on the internet. The diverse nature of opinions and sentiments expressed in tweets presents a unique opportunity for sentiment analysis, where understanding the emotions and attitudes of users towards various topics becomes crucial. Sentiment analysis on Twitter is a challenging task due to the unstructured and heterogeneous nature of the content, which can be positive, negative, or neutral in different contexts. In this paper, we present a comprehensive survey and comparative analysis of existing techniques for sentiment analysis, with a focus on utilizing the BERT (Bidirectional Encoder Representations from Transformers) model, a cutting-edge deep learning approach.},
        keywords = {BERT, Sentiment, Twitter, Django, Deep Learning.},
        month = {},
        }