Enhancing Accuracy in Social Media Sentiment Analysis through Comparative Studies using Machine Learning Techniques
Kottala Sri Yogi, Dankan Gowda, Divya Sindhu, Hariprasad Soni, Saptarshi Mukherjee, G. Madhu
Abstract
In the general scope of social media analytics, sentiment analysis is one of the most significant tools that can be employed to elucidate useful information from a vast amount of textual data. However, one of the primary problems that still persist with sentiment analysis is its accuracy because it is hard to understand precisely what a person meant on the vastness of the Web. In this research, these machine learning algorithms such as Naive Bayes (NB), Support Vector Machines and others are compared to determine its efficiency in enhancing the rate of accuracy in sentiment analysis across social media. Through data collection from various social media sources, which are preceded by stringent pre-processing techniques such as text normalization and feature extraction the performance of each model is evaluated. However, the findings present significant disparities in these models’ accuracies, illustrating their best conditions of operation. This research helps to fill the gap in literature by offering a more subtle idea of strengths and weaknesses of every machine learning methodology when used for sentiment analysis applications, which is useful information on how researchers as well as practitioners can improve analytical accuracy within social media context.