Published December 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

Enhancing Twitter Tweet Topic Understanding through Ensemble Learning

Creators

  • 1. Department of Computer Science, Manipal University Jaipur, Ijaipur (Rajasthan), India.

Contributors

Contact person:

  • 1. Department of Computer Science, Sanjay Ghodawat University, Kolhapur (Maharashtra), India.
  • 2. Department of Computer Science, Dr. A.P.J. Abdul Kalam Technological University, Lucknow (U.P), India.
  • 3. Department of Computer Science, Manipal University Jaipur, Ijaipur (Rajasthan), India.

Description

Abstract: Since Twitter’s introduction to social media of the hashtag as a content grouping label in 2007, the symbol and its associated usage as a classification la- bel have seen widespread adoption throughout social media and other platforms. While the content of a post can be conveniently classified using said post’s hash- tags, classifying posts that do not contain hashtags proves to be a much more challenging problem. In this paper, we propose a system for identifying a post’s hashtags using only the non-hashtag terms of the post and, by extension, address the issue of classifying the contents of posts that do not contain hashtags.

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Additional details

Identifiers

DOI
10.35940/ijitee.A9761.1213123
ISSN
2278-3075

Dates

Accepted
2023-12-15
Manuscript received on 06 November 2023 | Revised Manuscript received on 16 November 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023

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