An attempt of development of cross-domain recommender systems using extraction of emotions in text data

Abstract

In this paper, we report our attempt of development of cross-domain recommender systems using extracting emotions in text data. In our system, we focus on the emotions contained in reviews and comments on content, and aim to improve the accuracy of extracted emotions from text data. We constructed an emotion-words dictionary with 13001 words to extract eight emotion features from user’ s preference information obtained from text data. In experiments, we conducted cross-domain recommendation of video games from the game sales platform ”steam” based on extracting user’s emotions from comments on various videos in the video sharing service ”Nicovideo”.