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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>The automatic recognition of user sentiments through their music listening behavior is an important research task in cognitive studies. Whereas prior studies were conducted to identify the sentiment conveyed (or evoked) by a song that a user listens to at a particular time, we argue that a more effective method would be to identify the user\u2019s induced sentiment based on the comprehensive list of songs they have listened to (e.g., the sequence of music being played). However, recognizing the sentiment information induced by a playlist using machine learning techniques is much more challenging than identifying the sentiment induced by a single song, as it is difficult to obtain accurately labeled training samples for playlists. In this study, we developed the List\u2013Song Relationship Factorization (LSRF) model with the objective of efficiently identifying sentiments induced by playlists. This model employs two side information constraints: the sentiment similarity between songs, based on multimodal information, and the co-occurrence of songs in playlists. These constraints enable the simultaneous co-clustering of songs and playlists. The experimental results demonstrate that the proposed model efficiently and consistently identifies sentiment information evoked by either playlists or individual songs.<\/jats:p>","DOI":"10.1145\/3708346","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T10:24:46Z","timestamp":1733999086000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Songs: Analyzing User Sentiment through Music Playlists and Multimodal Data"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4365-7347","authenticated-orcid":false,"given":"Yipei","family":"Chen","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3377-1042","authenticated-orcid":false,"given":"Hua","family":"Yuan","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2274-3089","authenticated-orcid":false,"given":"Baojun","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8569-1784","authenticated-orcid":false,"given":"Limin","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0373-9815","authenticated-orcid":false,"given":"Yu","family":"Qian","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"issue":"4","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1177\/0305735606067168","article-title":"Songs and emotions: Are lyrics and melodies equal partners?","volume":"34","author":"Ali S. 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