Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain - Kumar, Anil; Chawla, Sonal (International Journal of Computer Sciences and Engineering, 2018)
In recent years, Internet is growing exponentially and so is the amount of learning resources. Due to overload of information, learners find it difficult to retrieve the appropriate learning resource. In academic domain, recommendation systems are facing problems in providing accurate suggestions to learner due to difference in types of learning resources, learner preferences, knowledge level and quality of the learning resource. In this context, the objective of this paper is four folds: Firstly, the paper discusses various techniques used in creation of recommendation system with a special focus on Academic Domain. Secondly, it compares and contrasts the existing recommender systems in practice today. Thirdly, the paper looks at the possibility of including Sentiment Analysis as an effective technique for recommending learning resources to the learners & it goes at length to give a sequential flow chart for a pilot study of book recommender system. Finally, the paper concludes by drawing the inferences on the introduction of sentiment analysis as a useful technique for recommendation system.