Twitter Sentiment Analysis

Authors

  • Gunjan R. Patil  Department of Computer Science Engineering, Amity University, Raipur, Chhattisgarh, India
  • Rishiraj Shrivastava  Department of Computer Science Engineering, Amity University, Raipur, Chhattisgarh, India
  • Advin Manhar  Assistant Professor, Department of Computer Science Engineering, Amity University, Raipur, Chhattisgarh, India

DOI:

https://doi.org/10.32628/CSEIT23903117

Keywords:

Twitter, Sentiment, Analysis, Opinion Mining, Social Media, Natural Language Processing

Abstract

Social media has entered further attention currently. Public and private opinions about a wide variety of subjects are expressed and spread continually via multitudinous social media. Twitter is one of the social media that's gaining trend. Twitter offers associations a fast and effective way to dissect guests ' perspectives toward the critical success in the request place. Developing a program for sentiment analysis is an approach to be used to computationally measure guests’ comprehension. This paper reports on the design of the sentiment analysis, rooting a vast quantum of tweets. Prototyping is used in this development. Results classify guests' perspectives via tweets into positive and negative, which is represented in a Graph. Still, the program has been planned to develop on a web operation system, but due to the limitation of Django which can be worked on a Linux Garcon or Beacon, further this approach needs to be done.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Gunjan R. Patil, Rishiraj Shrivastava, Advin Manhar, " Twitter Sentiment Analysis" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.520-528, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT23903117