Sentiment analysis for social media using SVM classifier of machine learning

Abstract

The communitys perspectives and comments are a valuable resource for businesses and other organizations. In the past, businesses used inefficient procedures. Now that social media is the new trend, it enables an unprecedented level of analysis and evaluation. This enables unprecedented analysis and evaluation of various factors. This enables unprecedented analysis and evaluation of a wide range of topics and components in different contexts and settings. Throughout business history, these strategies have been expected. This field of study is called sentiment analysis. SVM was used to analyze sentiment for this research project. One of these duties required an SVM(SVM). Support vector machines, or SVM, is a popular supervised machine learning algorithm for determining text polarity. SVM abbreviates support vector machines. Precision, recall, and F-measure are used to evaluate SVM using two datasets of pre-classified tweets. Tables and graphs are used to communicate research findings. This research classifies tweets about US-Airlines and performs sentiment analysis with an accuracy of 91.8 percent, precision of 91.3 percent, and recall of 82.3 percent, as well as the F1 of 86.9 percent.