The Use of the Convolutional Neural Network as an Emotion Classifier in a Music Recommendation System

Considering emotions and contextual factors in music recommendation: a systematic literature review

“…The authors assume to extract the user's emotion from some user-generated text (e.g., a Facebook post, a tweet), using text analysis techniques, making the emotional information available to the recommendation algorithm. The same occurs in the works in [84,36,101].…”

“…Mobile sensors or social networks could identify user behavior. Cheng and Shen [25] UH,CCI X 10 Helmholz et al [46] SEN, CCI X 11 Hong et al [49] UH, CCI, FEED X 12 Teng et al [117] MDI, UA, CCI, ML, CF X 13 Okada et al [96] ML, UB, MD X 14 Karlsson et al [67] UH, MDI X 15 Nirjon et al [95] SEN, UA, MDI X 16 Wang et al [126] MDI, UA, MD, ML X 17 Miller et al [88] CCI, MD X 18 Chang et al [21] MD, ML -19 Dias et al [32] UA, MD -20 Hansen et al [44] UH, CCI, ML -21 Wang et al [124] CF, MC, MD, UB -22 Assuncao and Neris [7] DIM, SI, MD Emotion X 23 Kang and Seo [66] DIM, MDI, CCI X 24 Abdul et al [2] DISC, SI, SIM, ML X 25 Chiu and Ko [26] DISC, SEN, ML, SI X 26 Iyer et al [57] SEN, FE X 27 Rosa et al [101] DIM, SN X 28 Guo et al [41] SEN, SIM X 29 Lehtiniemi and Holm [79] DIM, SI X 30 Helmholz et al [47] SI, DIM, DISC X 31 Andjelkovic et al [5] DIM, MD,SIM -32 Lopes et al [84] DISC, ML -33 Li and Liu [80] UB, MP -34 Padovani et al [97] DISC, SI, ML -35 Gilda et al [39] DISC, FE, MD ML -36 Vateekul et al [120] DIM, CF, SIM -37 Narducci et al [93] DISC, SN -…”

“…Although there are studies that classify emotions in dimensional models [30], generally, many studies describe emotions from a discrete perspective [93,84]. In the discrete perspective, the emotions are seen as a sum of categories that can be separated to obtain more refined and smaller subcategories.…”

“…The music recommendation approaches that consider the emotions, besides being aware of the user's emotion, also direct the efforts to identify the emotions of the songs. Usually, the approaches recover the music's emotion by extracting information from the songs [7,5,39,36], either by emotional tags, similarity, or audio signal processing accompanied by machine learning methods [2,26,84,80,120,30]. Most of these approaches require the audio of the music and depend on the user/specialist's intervention during certain phases of recognizing the music emotion, making it an even more significant challenge for recognizing emotions.…”

Emotional Characterization Mining in Music Performance and Its Guiding Role

“…Music is able to express emotions and stimulate emotional experiences, which have an impact on people's hearts and trigger physiological brain activity, which in turn affects their behavior and decision-making [3][4]. In addition to the wellknown effects of reducing anxiety and calming moods, music can also reduce pain, influence purchasing decisions, and promote the development of children's social skills and reading ability [5][6][7][8]. Music has been integrated into a variety of activities in people's daily lives, including work, study, sports, housework, and recreation and leisure [9][10][11].…”