# A Continuous Music Recommendation Method Considering Emotional Change
## 2 Related Research
### Emotion Analysis
However, owing to conceptual differences in individual emotions, there are limitations in accurately quantifying emotions with this method. When emotions are analyzed using music lyrics or audio information, there are limitations to evaluating the accuracy. Lyrics sometimes express emotions, but because they are generally lyrical and contain deep meanings, it is difficult to accurately predict emotions, and modern music, depending on the genre, has limitations in expressing emotions through audio information alone. Because emotions experienced when listening to music may vary based on individual emotional fluctuations and contexts, it is crucial to analyze how users experience emotions while listening to music. [PERSON] et al. [36] aimed to enhance music search performance by employing folksonomy-based mood tags and music AV tags. A folksonomy is a classification system that uses tags. Initially, they developed an AV prediction model for music based on [PERSON]'s emotion model to predict the AV values and assign internal tags to the music. They established a mapping relationship between the constructed music folksonomy tags and the AV values of the music. This approach enables music search using tags, AV values, and their corresponding mapping information. [PERSON] et al. [22] classified the emotional categories evoked by music into 13 emotions: joy (fun), irritation (displeasure), anxiety (worry), beauty, peace (relaxation), dreaminess, vitality, sensuality, rebellion (anger), joy, sadness (depression), fear, and victory (excitement). American and Chinese participants were recruited and asked to listen to various music genres, select the emotions they felt, and score their intensity. After analyzing the answers, the emotions experienced while listening to music were summarized into 13 categories. The study revealed a correlation between emotions and the elements of valence and arousal. In a past study [37] on the emotions evoked by paintings, users were asked to indicate the ratio of positive and negative emotions they felt toward the artworks, which are artistic content in addition to music. Additionally, in a study [38] on emotions in movies, emotions were derived from the analysis of movie review data. Various methods have been employed across various fields to analyze individual emotions. Psychology, dedicated to the study of emotions, actively conducts research on human emotions. Emotions are inferred through social factors, facial expressions, and voices recognized by humans [39; 40; 41]. Research has advanced from inferring emotions to predicting behavior based on other people's emotions and behavior [42; 43]. This confirms that humans can predict the emotions of others and that emotions can also predict future emotional states [26]. [PERSON] et al. [26] investigated the accuracy of mental models predicting emotional transitions. Participants rated the probability of transitioning between emotions based on 18 emotion words used in the study by [PERSON] et al. [13], assigning probabilities ranging from 0 to 100. They developed a mental model using average transition probabilities. To assess the accuracy of the mental model, they employed a Markov chain to measure how well the participants could predict real emotional transitions. The experiment utilized actual emotional history data, revealing significant prediction capability for emotions up to two levels deep. However, a limitation of the mental model presented in this study is its potential for errors due to the generalization of participants' individual emotions. Furthermore, because specific emotional transitions were evaluated using fragmented probabilities through one-to-one matching, it lacks consideration of the user's complete emotional history or temporal aspects. More accurate predictions could be achieved by predicting emotions based on an individual's personalized emotional transition probabilities. Additionally, brainwave data, facial expressions, and psychological tests were used to analyze user emotions.