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.

媒体，尤其是电影的消费正在增加，并受到多种因素的影响。影响媒体消费选择的一个重要且被忽视的因素是用户的情绪状态以及基于它的决策。为了将这个因素包含在电影推荐过程中，我们提出了一个知识图来表示电影领域中的人类情感。知识图是通过使用机器学习技术从预先存在的电影评论中提取情感来构建的。为了展示如何使用知识图谱，我们开发了一个聊天机器人原型。聊天机器人的推理机制通过将从聊天消息中提取的用户情绪与知识图相结合，为用户推导出电影推荐。