Comparative Study and Framework Design for Twitter Sentiment Detection and Categorization Utilizing Machine Learning Methodologies

Abstract

The paper is an exhaustive comparative review which gives a way forward in classifying and analyzing sentiments in a twitter data, with the support vector machines (SVM) as the primary analytical tool. The research directs into the depth of sentiment analysis that plays a major role thereby conveying public opinions, trends, and social dynamics of digital platforms like Twitter. Through application of SVN, which is particularly effective in dealing with high dimensional category of data, it gives relatively better knowledge on the context of How Twitter sentiment is tested in the Twitter's short and terse content. The research examines the approaches to sentiment analysis systematically, pointing at vaguely of those methods and of their suitability for the Twitter environment. In particular, this approach underlines that SVM can handle the complexities of Twitter data such as slang words, abbreviations, and emoticons which are quite hard for a text analysis system to manage. The study uses a framework design which is appropriate for the task and takes into account typical operations such as tokenization, stemming, and removal of stop words and this is very crucial for the adjustment of the sensitive model input, using SVM. The results of this empirical assessment will show that SVM is better than NB in some situations, such as when you need to deal with sparse and high-dimensional feature spaces that are typical of Twitter data. The research along with the same walks us through the jeopardy of choosing different kernel functions and finding the ways to set the parameters in SVM which can lead to optimization of the classifier performance. Thus, it also shows an intense path of solving the old challenge. Additionally, the study aims at the demonstration of SVM application in practical scenarios through sentimental analysis. This is to explain how sentiment analysis method can be utilized for business decision-making, political analysis, and social research. It demonstrates the possible effectiveness of SVM-informed sentiment analysis in: assessing the standings of public opinion, diagnosing brand reputation, and the illumination of concerns of the people through the point of view of Twitter.In conclusion, this study not only sheds light on the comparative effectiveness of various sentiment analysis approaches on Twitter but also offers a robust design framework using SVM, contributing valuable insights to the field of text analysis and data mining..