# A Continuous Music Recommendation Method Considering Emotional Change
## 2 Related Research
### Emotion Analysis  
Various studies have analyzed emotions in content. [PERSON] et al. [33] classified the music emotions using audio information and lyrics, selecting four levels of emotions: angry, happy, sad, and relaxed. Better performance in emotion analysis was shown when both audio and lyrics were used compared with lyrics alone. [PERSON] et al. [34] also classified music emotions using audio and lyrics. They created emotional categories using tag information and conducted experiments comparing three methods: audio, lyrics, and audio plus lyrics. Performance varied by category. In categories such as happy and calm, the best performance was achieved when audio was used. On the other hand, in the romantic and angry categories, the best performance was achieved when lyrics were used. In a previous study [20] focusing on visualizing music based on the emotional analysis of lyric text, the lyric text was linked to video visualization rules using Google's natural language processing API. The emotional analysis extracted score and magnitude values, which were mapped to valence and arousal to determine the location of each emotion. The score represents the emotion and is interpreted as positive or negative, and the magnitude represents its intensity. Based on this, six emotions (happy, calm, sad, angry, anticipated,  
Figure 2: [PERSON]’s emotion model [29]. and surprised) were classified into ten emotions according to their intensity levels. Consequently, we propose a rule for setting ranges based on values obtained from Google's natural language processing Emotion Analysis API. In a previous study by [PERSON] et al. [32] analyzing the relationship between mood and color according to music preference, multiple users were asked to select colors associated with mood and mood words for music. Emotional coordinates were calculated using [PERSON]'s model based on the input values of the emotional intensity experienced by several users while listening to music. The sum of the intensity of each emotion for a specific piece of music among multiple users was calculated using Equation (1). \[ed_{i}^{s}=\sum_{u=1}^{n}data_{u,i}^{s}\left\{ \begin{array}{l}i=1,\;2,\;3,\; \ldots,\;12\\ n\;:number\;of\;volunteers\end{array} \right. \tag{1}\]  
Here, \(ed_{i}^{s}\) represents the sum of emotions related to music, and \(data_{u,\;i}^{s}\) denotes the level of emotions input by the user regarding the music. Using the results from Equation (1), the coordinate values for each emotion in the arousal and valence (AV) space were computed, as shown in Equation (2). \[x_{i}=ed_{i}^{s}\times\cos(f\theta_{i}),y_{i}=ed_{i}^{s}\times\sin(f\theta_{i}) \tag{2}\]  
Here, \(x_{i}\) represents the valence value of emotion \(i\) in the AV space, \(y_{i}\) represents the arousal value of emotion in the AV space, and \(i\) represents the angle of emotion in the AV space. \(f\theta_{i}\) is defined recursively as \(f\theta_{i-1}+30(2\leq i\leq 12)\), and \(f\theta_{1}\) is \(15^{\circ}\). The average of the computed coordinate values for each emotion was then determined using Equation (3) to identify the representative emotion of the music. \[\overline{x}=\frac{1}{12}\sum_{i=1}^{12}x_{i},\overline{y}=\frac{1}{12}\sum_{ i=1}^{12}y_{i} \tag{3}\]  
In a previous study by [PERSON] et al. [30], they designed a method to predict emotions by analyzing various elements of music. This method involved deriving an emotion formula that incorporates weights for five music elements: tempo, dynamics, amplitude change, brightness, and noise. These elements were mapped onto a two-dimensional emotion coordinate system represented by X and Y coordinates. Next, the study involved plotting these X and Y values onto on a two-dimensional plane and drawing a circle to determine the minimum and maximum radii that could encompass all eight emotions. The probability of each emotion was then calculated based on the proportion of the emotion's area within the circle. These probabilities were compared with emotions experienced by actual users through a survey.