Sentiment Analysis on Social Media Data using Support Vector Machine (SVM)

Abstract

A social media platform serves as a conduit for people to communicate with one another. Twitter is one of the most widely used social media platforms. Users of all kinds should express their thoughts and opinions on different aspects of daily life. As a result, social media platforms are viewed as valuable data sources for opinion mining. Such information is ideal for sentiment analysis. The computational study of an entity's thoughts, perceptions, and emotions is known as sentiment analysis or opinion mining. The entity may be used to describe an individual, an event, or a subject. The proposed work uses a Support Vector Machine to extract sentiment from tweets about a US airline. The Support Vector Machine (SVM) will find the separated hyperplane that maximizes the margin between the various groups. As a feature extractor, N-gram (bigram and trigram) is used, and the proposed approach's output is calculated in terms of accuracy.