Group Recommendation System Using Sentimental Analysis for User Satisfaction

Studies have shown that the existing recommendation systems provide misleading recommendations that actually do not meet the expectations of the tourists. Despite the fact that there have been efforts to build group recommendation system, they are primarily focused on offline settings. Moreover, combining individual suggestions into group recommendations or combining individual preferences into group preferences is a crucial element of group recommendation. Tourist recommendation system using sentimental analysis has given a better opportunity to the developer to know the user opinions for travelling. By extracting sentiment from group members, this work proposes an improved technique for group recommendation that boosts both the effectiveness and satisfaction of the users. The proposed technique uses clustering and similarity scores for recommending points of interests to the tourist groups. For performance analysis the proposed technique has been implemented using Python. Performance comparison shows that the proposed scheme outperforms the baselines with respect to accuracy.