Recent Trends in Opinion Mining using Machine Learning Techniques

Opinion mining is a sub-field of data mining and natural language processing that concerns extracting users' opinions and attitudes towards products or services from their comments on the web. Human beings rely heavily on their perceptions. When making a choice, other people's perspectives are taken into account. Currently, billions of Internet users communicate their opinions on several disciplines via journals, discussion forums, and social media sites. Companies and institutions are constantly interested in hearing what the general public thinks regarding their services and goods. It is critical in e-commerce and e-tourism to dynamically evaluate the vast number of user data available on the Internet; as a result, it is essential to establish ways for analysing and classifying it. Opinion mining, also known as sentiment classification, autonomously extracts opinions, views, and feelings through literature, audio, and data inputs using natural language processing. This paper provides an understanding of the machine learning strategies for classifying comments and opinions. This paper compares various machine learning-based opinion mining techniques such as Naive Bayes, SVM, genetic algorithm, decision tree, etc.