Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data

  • Muhammad Adeel Abid Faculty of Information Technology, Khwaja Fareed University of Engineering and Information Technology, 64200 Rahim Yar Khan Pakistan
  • Muhammad Faheem Mushtaq Department of Artificial Intelligence, The Islamia University of Bahawalpur, 63100 Bahawalpur Pakistan
  • Urooj Akram Department of Artificial Intelligence, The Islamia University of Bahawalpur, 63100 Bahawalpur Pakistan
  • Mateen Ahmed Abbasi Faculty of Information Technology, Khwaja Fareed University of Engineering and Information Technology, 64200 Rahim Yar Khan Pakistan
  • Furqan Rustam Faculty of Information Technology, Khwaja Fareed University of Engineering and Information Technology, 64200 Rahim Yar Khan, Pakistan

Abstract

Twitter has become the foremost standard of social media in today’s world. Over 335 million users are online monthly, and near about 80% are accessing it through their mobiles. Further, Twitter is now supporting 35+ which enhance its usage too much. It facilitates people having different languages. Near about 21% of the total users are from US and 79% of total users are outside of US. A tweet is restricted to a hundred and forty characters; hence it contains such information which is more concise and much valuable. Due to its usage, it is estimated that five hundred million tweets are sent per day by different categories of people including teacher, students, celebrities, officers, musician, etc. So, there is a huge amount of data that is increasing on a daily basis that need to be categorized. The important key feature is to find the keywords in the huge data that is helpful for identifying a twitter for classification. For this purpose, Term Frequency-Inverse Document Frequency (TF-IDF) and Loglikelihood methods are chosen for keywords extracted from the music field and perform a comparative analysis on both results. In the end, relevance is performed from 5 users so that finally we can take a decision to make assumption on the basis of experiments that which method is best. This analysis is much valuable because it gives a more accurate estimation which method’s results are more reliable.

Published
Jan 1, 2023
How to Cite
ABID, Muhammad Adeel et al. Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 42, n. 1, p. 88-94, jan. 2023. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2696>. Date accessed: 06 feb. 2023. doi: http://dx.doi.org/10.22581/muet1982.2301.09.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License