The Automation of Sentiment Analysis on Cross-Platform Social Media Data: A Comparative Study of Techniques and Tools

Abstract

Sentiment analysis from social media has become an indispensable tool for tracking company reputation, analyzing public opinion, and learning about customer behavior. However, human analysis is severely hampered by the massive volume of data created across numerous social media platforms. This study offers a thorough comparison of methods and resources for automated sentiment analysis on data from cross-platform social media platforms. We assess how well different lexicon-based strategies, hybrid approaches, and machine learning algorithms perform on various social media platforms, such as Facebook, Instagram, and Twitter. Furthermore, we evaluate how well-suited common sentiment analysis tools like NLTK, TextBlob, and Stanford CoreNLP are to handle the particularities of social media data, like emoticons, slang, and abbreviations. Our research offers insightful information about the advantages and disadvantages of each strategy as well as recommended procedures for producing trustworthy and accurate sentiment analysis results on a variety of social media platforms.