Result Analysis on Twitter Sentiment Analysis using BERT Algorithm

  • Unique Paper ID: 161238
  • Volume: 10
  • Issue: 3
  • PageNo: 11-19
  • 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.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@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 = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 3
  • PageNo: 11-19

Result Analysis on Twitter Sentiment Analysis using BERT Algorithm

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