Personalized Word Recommendation System Using Sentiment Analysis

Online Social Networks is a place where a user is truly free to express himself or herself, and it is observed to be so. Social networks are used by users for not only socializing but to buy or sell products. This user behavior is quite adamant from the type of comments they post on different social networks. The word recommendation system is still not personalized rather generalized for all different websites. It can, however, be personalized by the use of sentiment analysis and the model here has done the same here. The model has used subjectivity and polarity for making a personalized recommender system by analyzing the behavior and classifying them. This provided the model with two greater ways to recommend, i.e., recommending on the basis of the user as prevalent from their comments and also by the topics been discussed by them.