loading page

Sentiment Analysis on Social Media using Machine Learning Approach
  • Erick Omuya,
  • George Okeyo,
  • Michael Kimwele
Erick Omuya
Machakos University

Corresponding Author:[email protected]

Author Profile
George Okeyo
Carnegie Mellon University
Author Profile
Michael Kimwele
Jomo Kenyatta University of Agriculture and Technology
Author Profile


Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.
27 Oct 2021Submitted to Engineering Reports
01 Nov 2021Submission Checks Completed
01 Nov 2021Assigned to Editor
06 Nov 2021Reviewer(s) Assigned
10 Jan 20221st Revision Received
11 Jan 2022Assigned to Editor
11 Jan 2022Submission Checks Completed
17 Jan 2022Reviewer(s) Assigned
08 Feb 2022Editorial Decision: Revise Major
09 Apr 20222nd Revision Received
11 Apr 2022Submission Checks Completed
11 Apr 2022Assigned to Editor
11 Apr 2022Reviewer(s) Assigned
19 May 2022Editorial Decision: Revise Minor
25 May 20223rd Revision Received
26 May 2022Submission Checks Completed
26 May 2022Assigned to Editor
06 Jun 2022Editorial Decision: Revise Minor
02 Jul 20224th Revision Received
04 Jul 2022Submission Checks Completed
04 Jul 2022Assigned to Editor
02 Aug 2022Editorial Decision: Revise Minor
08 Sep 20225th Revision Received
09 Sep 2022Assigned to Editor
09 Sep 2022Submission Checks Completed
23 Sep 2022Editorial Decision: Accept