A Framework for Classifying Imbalanced Tweets Using Machine Learning Techniques

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.