Machine Learning Techniques for Sentiment Analysis: A Review

Abstract

With the advancement of informal community and Web 2.0, individuals not just devour content by downloading on the web yet contributing and producing new substance. Individuals turned out to be increasingly anxious to communicate and impart their insights on web viewing every day exercises just as nearby or worldwide issues. Because of the multiplication of web-based life, such as Facebook, Twitter, YouTube and others, supposition examination develops quickly. The number of recordings accessible on the web and somewhere else is consistently developing and with this the requirement for viable strategies to process the tremendous measure of multimodal data shared through this media. Right now, it centers around the investigation of different procedures are applied on different datasets. Additionally, condense the report of strategies most as often as possible utilized in AI for notion examination. In this paper, widely used machine learning methods of sentiment analysis are analyzed and summarized as various technologies applied on various authors’ datasets.