Volume 10, Number 10, July 2020

Text-based Emotion Aware Recommender

  Authors

John Kalung Leung, Igor Griva and William G. Kennedy, George Mason University, USA

  Abstract

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.

  Keywords

Context-Aware, Emotion Text Mining, Affective Computing, Recommender Systems, Machine Learning.

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