Sentiment analysis

Abstract

Sentiment analysis, or opinion mining, involves computationally studying people's opinions, attitudes, and emotions toward an entity. There are three approaches to conducting a sentiment analysis: lexicon-based, machine learning, and hybrid. The lexicon-based approach utilizes methods like the directory-based approach and the corpus-based approach. The machine learning approach categorizes documents into predefined sentiment categories, employing algorithms like Naive Bayes and Support Vector Machines for supervised learning and models such as Valence Aware Dictionary and Sentiment Reasoner for unsupervised learning. In the sport industry, sentiment analysis is a valuable tool for understanding consumer attitudes toward sport brands, improving service satisfaction, increasing fan engagement, and evaluating new applications. Scholars and practitioners benefit from insights into disciplines such as sport ethics, economics, psychology, computer science, and journalism. Sentiment analysis offers nuanced perspectives on fans' opinions, providing a useful tool for decision-making and strategic action in the sport industry.