Twitter sentimental analysis using machine learning

Abstract

This research paper aims to explore the effectiveness of machine learning algorithms in analyzing sentiment on Twitter. The study utilizes a dataset of tweets collected from various sources, which were then preprocessed to remove noise and irrelevant data [4, 5]. To categorize the tweets as positive, negative, or neutral, a number of machine learning techniques were used, such as logistic regression and Naive Bayesian [1]. The efficiency of these algorithms is also assessed in the study using a number of criteria, including accuracy, precision, recall, and F1 score. The results indicate that machine learning algorithms are effective in analyzing sentiment on Twitter, with Naive Bayes providing the best performance [18]. The results of this study have significant ramifications for companies and organizations looking to track consumer opinion of their goods or services [7]. This paper examines the problem of analyzing sentiment in Twitter by examining the tweets' expressed sentiments—whether they be favourable, negative, or neutral. Natural language processing methods will be used to analyze the messages that are tweeted.