Comparison of Sentiment Analysis Algorithms Using Twitter Dataset for Real Time Analysis of Cricket Sport

In the area of sports sentiment analysis, particularly focused on cricket, this study presents a comprehensive comparison of three prominent machine learning algorithms: Support Vector Machines (S VM), Decision Trees, and Random Forests. The objective of this research work is to evaluate the efficacy of these algorithms in discerning sentiment patterns from diverse cricket-related textual data. The study utilizes Python programming language with the Spyder IDE for implementation. The data incorporates a variety of any cricket-related sources, including match commentaries, fan discussions etc. This study assesses the proposed model's performance in terms of accuracy, precision, recall, and Fl-score, considering the nuances of sentiment analysis in the context of cricket sports.