The recommendation system is a widely used information filtering system that is designed to recommend items to the user based on the interest of users. Personalization in a recommendation system is the new formation that is designed to suggest customized preferences of items to an individual based on the historical information of the user.
Opinion mining for a personalized recommendation system helps analyze and classify the opinion polarities of the reviews. However, opinion mining in personalized recommendation strictly focuses on the opinions of the users, and it is difficult to analyze the huge amount of opinionated data. It is important to extract the granular level features from the user review to get optimal suggestions. Aspect level opinion mining is capable of analyzing the polarity of opinions in fine-grained and extracting the different features of the items. Efficient and personalized suggestions are generated by identifying the opinions for the specific aspect of users. Aspect-based opinion mining enhances the accuracy of the personalized recommendation system and provides recommendations based on the tendency of the user towards different features of the items.