Machine Learning Approaches to Sentiment Analysis in Social Networks using Political Tweets

Abstract

Abstract: Online social networks offer a quantitative assessment of people's psychological behaviour and aid in the general analysis of social or political concerns. In text mining research, opinions, attitudes, and subjectivity in text and other expressions are ascertained by a computational method. Furthermore, the majority of approaches try to simulate word syntactic information without taking sentiment into account. A brief description of the various machine learning (ML) models utilized in sentiment analysis is provided in this paper. Additionally, suggest a productive modular strategy that will provide exact correctness when testing and evaluating the Twitter data. In today's world, when national and international leaders are important, political reviews linked to Twitter data collection are more prevalent. Our work's goal is to find solutions by analysing and contrasting various approaches. According to a simulation study, there is a practical approach to comprehensively analyse and use a political twitter dataset regarding an international leader, while concentrating on additional sentiment dataset validation to increase the precision of tweet sentiment analysis.