| Summary: | 博士 === 國立政治大學 === 資訊管理學系 === 107 === Emotional products, such as music, movies, novels… etc., are quite different from functional products. Because the evaluation of emotional products is related to personal feelings, emotional products have much more kinds of user reviews on the Internet than functional products. People will more likely choose an emotional product because of the content and People will more likely use different emotional products then functional products in the different situations.
In the past researches, an opionion mining based recommendation system usually only use web reviews to recommend products, there were not many studies use both user reviews and the content of the product at the same time. This kind of recommendation system was also only driven by positive or negative tendency rules, and there are also few discussions to find out the emotional rules, such like “this music is very happy to hear.” In addition, the traditional recommendation system usually ignores the fact that people's preference for emotional products will be very different in different contexts; people who like to listen to rock music during exercise are not necessarily like rock music at bedtime. Under such a background, how to establish a context-awareness recommendation system that can effectively and effectively help people to choose emotional products by using both online reviews and product content is very important.
In this study, we took pop music as a case of emotional products and we had proposed a recommendation prototype system use both web reviews opinion mining and a lyrics and sound content-based tags classifier to be the recommendation sources. After data collection, analysis and training processes, the overall web reviews opinion classifier accuracy average F1 score is 70.09%; the music content emotional tags classifier accuracy marco-average F1 score is 74.89% and micro-average F1 score is 80.13%.
Using these two classifiers, a context-aware prototype system was proposed in this study, we had completed a context-aware product recommendation system that meets the characteristics of emotional goods. Finally, we designed three experiments to verify the effectiveness of the emotional classifier and context-aware system.
In the emotional music experiment, we found that by using joy and calm music content classifier trained in this study can effectively help to reduce the sadness and increase the degree of control. In addition, we can also found that the recomendtion order joy-clam and clam-joy was no significant different in emotion regulation, but it will affect satisfaction. From the interview data of the exercise and sleeping context experiments, we got some positive feedback results for the context-aware prototype system of this study. This will be some help in developing similar systems in the future.
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