Text-based Emotion Aware Recommender.

We extend the concept of using an active user's emotion embeddings and movies' emotion embeddings to evaluate a Recommender top-N recommendation list as illustrated in a previous paper to encompass the emotional features of a film as a component of building Emotion Aware Recommender Systems. Using textual movie metadata, we develop a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We then apply the movie emotion embeddings obtained from classifying the emotional features of movie overviews by the Tweets Emotion Classifier, which we have developed to add an emotional dimension of embeddings for the Recommender. Emotion Aware Recommender's top-N recommendations list shows intrigue results which are quite different from its peer. We reckon that the Emotion Aware Recommender top-N list, which matches the active user's emotional profile, is useful for providing serendipity recommendations and remedying the cold start problem commonly present in Recommender.