The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Framework for Classifying Imbalanced Tweets Using Machine Learning Techniques
|
|
Author(s): R. Srinivasan (Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, India)and Rajeswari D. (Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, India)
Copyright: 2023
Pages: 17
Source title:
Perspectives on Social Welfare Applications’ Optimization and Enhanced Computer Applications
Source Author(s)/Editor(s): Ponnusamy Sivaram (G.H. Raisoni College of Engineering, Nagpur, India), S. Senthilkumar (University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India), Lipika Gupta (Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, India)and Nelligere S. Lokesh (Department of CSE-AIML, AMC Engineering College, Bengaluru, India)
DOI: 10.4018/978-1-6684-8306-0.ch001
PurchaseView on the publisher's website for pricing and purchasing information.
|
Abstract
The research work presented focuses on utilizing social media platforms as a source of data to diagnose depression-related issues. The popularity of social platforms such as LinkedIn, Instagram, Twitter, YouTube, and Facebook, gave researchers an opportunity to analyse user experiences and gain insights into depression. Depression is a significant problem that affects individuals' lives, disrupts normal functioning, and impacts their perspectives. The primary objective of this research is to employ machine learning (ML) approaches for classifying tweets. Additionally, the research addresses the issue of data imbalance by using sampling techniques. This research work utilizes a sampling technique to normalize the dataset. The study explores four techniques that helps to extract meaningful information from the tweets. The research work conducts an empirical study to evaluate the performance of various ML techniques. Based on the experimental results, it is found that the AdaBoost classifier with the BoW feature extraction technique achieves the best results among all the classifiers tested.
Related Content
|
G. Surekha, Edwin Shalom Soji.
© 2026.
20 pages.
|
|
P. Vidhya, S. Silvia Priscila.
© 2026.
28 pages.
|
|
G. Surekha, Edwin Shalom Soji.
© 2026.
20 pages.
|
|
P. Kiruthiga, S. Silvia Priscila.
© 2026.
28 pages.
|
|
C. Ashwini, S.T.V.T. Anantha Krishnama Charyulu, N. Avinash Chowdary, S.T.V. Sathvik, A. Thenmozhi, Sureshkumar Somayajula, Muhammad Saleem.
© 2026.
26 pages.
|
|
Divya Divya, Kamlesh Kumar Yadav.
© 2026.
20 pages.
|
|
S. Kiruthika, S. Silvia Priscila.
© 2026.
24 pages.
|
|
|