Representing emotions with knowledge graphs for movie recommendations

Consumption of media, and movies in particular, is increasing and is influenced by a number of factors. One important and overlooked factor that affects the media consumption choices is the emotional state of the user and the decision making based on it. To include this factor in movie recommendation processes, we propose a knowledge graph representing human emotions in the domain of movies. The knowledge graph has been built by extracting emotions out of pre-existing movie reviews using machine learning techniques. To show how the knowledge graph can be used, a chatbot prototype has been developed. The chatbot's reasoning mechanism derives movie recommendations for the user by combining the user's emotions, which have been extracted from chat messages, with the knowledge graph. The developed approach for movie recommendations based on sentiment represented as a knowledge graph has been proven to be technically feasible, however, it requires more information about the emotions associated with the movies than currently available online. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Consumption of media, especially movies, is on the rise and is influenced by various factors. An important but often overlooked factor is the emotional state of users. A knowledge graph representing human emotions in movies has been proposed, with a chatbot prototype developed to provide movie recommendations based on user emotions extracted from chat messages. While technically feasible, more information on movie emotions is needed.