Twitter Sentiment Analysis Using Different Machine Learning Techniques
摘要
In this business-oriented world, the data generated on mass communication platforms is stellar. To extract something valuable from this data using trivial methods can be a devastating process, so using modern sentiment analysis algorithms with the help of natural language processing and machine learning, namely naive Bayes classifier, support vector machine (SVM), decision trees, VADER, and regression, can reduce the time by a large fraction. The algorithms can segregate and differentiate all text-based opinions as humanly as possible. The main contributions of this paper include the comparison and efficiency mapping of various sentiment analysis (SA) algorithms and their related areas.