Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Abstract

Social media has revolutionized the way people disclose their personal health concerns and express opinions on public health issues. In this paper a new approach for multi-class sentiment classification using supervised learning techniques. The aim of this multi-class sentiment classification is to assign the healthcare Tweets automatically into predetermined categories on the basis of their linguistic characteristics, their contents, and some of the words that characterize each category from the others. Briefly, relevant health datasets are collected from Twitter using Twitter API; then, use of the methodology is illustrated and evaluated against one with only three different algorithms was used, to improve the accuracy of Decision Trees, SMO, and K-NN classifiers. Many experiments accomplished to prove the validity and efficiency of the approach using datasets tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes