Sentiment Analysis of Tweets: A Study of Public Opinion (KEY IJP************493)

• Varun Bhalla,Arun Bhalla,Anuj Maurya

Abstract

With the increasing popularity of social media, Twitter has emerged as a significant platform where users express their opinions on diverse topics, including brands, politics, and current events. Twitter Sentiment Analysis (TSA) has garnered extensive research interest as it enables the extraction of insights from public sentiment and opinionated text. This review paper provides a comprehensive overview of the key approaches, techniques, and challenges in Twitter Sentiment Analysis. TSA methods are broadly classified into machine learning-based and lexicon-based approaches, each with unique advantages and limitations in handling short and informal text formats of tweets. Machine learning approaches often involve algorithms like Naive Bayes, Support Vector Machines (SVM), and neural networks, whereas lexicon-based approaches rely on sentiment lexicons for polarity classification. Additionally, we discuss preprocessing steps essential for handling Twitters unique characteristics, such as slang, abbreviations, and hashtags, which significantly impact TSA accuracy. This paper also explores recent advancements, including deep learning techniques and hybrid models, which improve sentiment classification accuracy. The review concludes by identifying future research directions in handling multilingual content, real-time sentiment tracking, and addressing the challenges posed by sarcasm and ambiguity in tweets.Twitter sentiment analysis is a Web Application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets which makes the processing difficult. The tweet format is very small which generates a whole new dimension of problems like use of slang, abbreviations etc. In this paper, we aim to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach.