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article · 2023

Mining Opinions by Means of User Reactions

Sarthak Srivastava, Anchal Kamra

Cited by 0Abstract and details only

Abstract

Nowadays, the social network is enhanced through people interacting with each other and sharing feelings, comments, views, experiences, opinions, and comments about anything. Since there are so many web resources available in digital form for expressing one's viewpoint, like review sites, blogs, and forums on discussion, the importance of sentimental analysis and opinion mining has significantly grown up. Sentiment Analysis is a subfield of Natural Language Processing that is concerned with determining the views of individuals about an article or subject. Sentiment Analysis, frequently recognized as Opinion Mining, is the process of gathering and analyzing customer opinions on a product expressed in blog posts, comments, or reviews. At present, there are some challenges in opinion mining techniques. Firstly, is an opinion that is taken as good in a situation but can also be considered bad in another circumstance? Secondly, the public doesn’t usually put forward opinions in an alike manner. This paper presents an overview of the work done in the past and also about the proposed work.

Cite this paper

APA 7

Srivastava, S., & Kamra, A. (2023). Mining Opinions by Means of User Reactions. https://doi.org/10.1109/icccnt56998.2023.10307744

MLA 9

Srivastava, Sarthak, and Anchal Kamra. “Mining Opinions by Means of User Reactions.” vol. 69, 2023, pp. 1–6, https://doi.org/10.1109/icccnt56998.2023.10307744.

Chicago

Srivastava, Sarthak, and Anchal Kamra. 2023. “Mining Opinions by Means of User Reactions.” https://doi.org/10.1109/icccnt56998.2023.10307744.

Harvard

Srivastava, S. and Kamra, A. (2023) ‘Mining Opinions by Means of User Reactions’. Available at: https://doi.org/10.1109/icccnt56998.2023.10307744.

BibTeX
@article{srivastava2023mining,
  title = {{Mining Opinions by Means of User Reactions}},
  author = {Srivastava, Sarthak and Kamra, Anchal},
  volume = {69},
  pages = {1--6},
  year = {2023},
  doi = {10.1109/icccnt56998.2023.10307744}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

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