Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach
Yu‐Xing Qi, Zahratu Shabrina
Abstract
Social media platform such as Twitter provides a space where users share their thoughts and opinion as well as connect, communicate, and contribute to certain topics using short, 140 characters posts, known as tweets . This can be done through texts, pictures, and videos, etc., and users can interact using likes, comments, and reposts buttons. According to Twitter ( https://investor.twitterinc.com ), the platform has more than 206 million daily active users in 2022, which is defined as the number of logged accounts that can be identified by the platform and where ads can be shown. As more people contribute to social media, the analysis of information available online can be used to reflect on the changes in people's perceptions, behavior, and psychology (Alamoodi et al. 2021 ). Hence, using Twitter data for sentiment analysis has become a popular trend. The growing interest in social media analysis has brought more attention to Natural Languages Processing (NLP) and Artificial Intelligence (AI) technologies related to text analysis.