{"id":6879,"date":"2026-05-17T22:50:44","date_gmt":"2026-05-17T14:50:44","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6879"},"modified":"2026-05-19T11:32:02","modified_gmt":"2026-05-19T03:32:02","slug":"jase-202609-32-039","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-039","title":{"rendered":"Music Sentiment Analysis Based on Multi-Modal Intelligent Computing and Deep Learning"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Lu Huang<\/i><\/a> <\/p>\n\n\n\n

JiLin Provincial Institute of Education, Changchun, Jilin 130022, China<\/p>\n<\/div>\n\n\n\n

\n

Received: February 26, 2026
Accepted: April 4, 2026
Publication Date: May 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Accuracy with the number of iterations <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.039<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

The study of intelligent computing and deep learning has become a prominent research topic among both industrial and academic researchers in recent years. As a typical form of intelligent computing and deep learning, with ongoing advancements in affective computing, the close connection between deep learning, multi-modal information, and emotion has gradually garnered the attention of researchers. Existing methods still exhibit many shortcomings in the perception, understanding, and expression of machine emotions. A computational model of emotion that integrates emotion perception, information fusion, and deep learning is proposed. The model is a deep learning-oriented network perception model that accepts visual, auditory, and textual inputs to achieve an understanding of uncertain emotions. Experiments demonstrate that the model performs well in various multi-modal emotion computations. The studies presented in this paper provide important guidance for the application of both multi-modal intelligent computing and deep learning.<\/p>\n\n\n\n

Keywords: Intelligent Computing; Deep Learning; Music Sentiment Analysis; Multi-modal Information<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
    \n
  1. [1] D. Han, Y. Kong, J. Han, and G. Wang, (2022) \u201cA survey of music emotion recognition\u201d Frontiers of Computer Science 16: 1\u201311. DOI: 10.1007\/s11704-021-0569-4.<\/li>\n
  2. [2] Y. Hu, (2022) \u201cMusic emotion research based on reinforcement learning and multimodal information\u201d Journal of Mathematics 2022: 1\u201310. DOI: 10.1155\/2022\/2446399.<\/li>\n
  3. [3] L. M. G\u00f3mez and M. N. C\u00e1ceres. \u201cApplying data mining for sentiment analysis in music\u201d. In: International Conference on Practical Applications of Agents and Multi-Agent Systems. Cham: Springer, 2017, 198\u2013205. DOI: 10.1007\/978-3-319-61578-3_20.<\/li>\n
  4. [4] K. Napier and L. Shamir, (2018) \u201cQuantitative sentiment analysis of lyrics in popular music\u201d Journal of Popular Music Studies 30: 161\u2013176. DOI: 10.1525\/jpms.2018.300411.<\/li>\n
  5. [5] S. Shukla, P. Khanna, and K. K. Agrawal. \u201cReview on sentiment analysis on music\u201d. In: 2017 International Conference on Infocom Technologies and Unmanned Systems (ICTUS). IEEE, 2017, 777\u2013780. DOI: 10.1109\/ICTUS.2017.8286111.<\/li>\n
  6. [6] W. Chen, (2022) \u201cA novel long short-term memory network model for multimodal music emotion analysis in affective computing\u201d Journal of Applied Science and Engineering 26: 367\u2013376. DOI: 10.6180\/jase.202303_26(3).0008.<\/li>\n
  7. [7] R. Kaur and S. Kautish. \u201cMultimodal sentiment analysis: A survey and comparison\u201d. In: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022, 1846\u20131870. DOI: 10.4018\/978-1-6684-6303-1.ch098.<\/li>\n
  8. [8] D. Ghosal, M. S. Akhtar, D. Chauhan, S. Poria, A. Ekbal, and P. Bhattacharyya. \u201cContextual inter-modal attention for multi-modal sentiment analysis\u201d. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 3454\u20133466. DOI: 10.18653\/v1\/D18-1382.<\/li>\n
  9. [9] B. R. Gudivaka, (2021) \u201cDesigning AI-assisted music teaching with big data analysis\u201d Current Science Humanities 9: 1\u201314.<\/li>\n
  10. [10] J. Liu, P. Zhang, Y. Liu, W. Zhang, and J. Fang, (2021) \u201cSummary of multi-modal sentiment analysis technology\u201d Journal of Frontiers of Computer Science and Technology 15: 1165. DOI: 10.3778\/j.issn.1673-9418.2012075.<\/li>\n
  11. [11] H. Wen, S. You, and Y. Fu, (2021) \u201cCross-modal context-gated convolution for multi-modal sentiment analysis\u201d Pattern Recognition Letters 146: 252\u2013259. DOI: 10.1016\/j.patrec.2021.03.025.<\/li>\n
  12. [12] A. S. Alqarafi, A. Adeel, M. Gogate, K. Dashitpour, A. Hussain, and T. Durrani. \u201cTowards Arabic multi-modal sentiment analysis\u201d. In: International Conference on Communications, Signal Processing, and Systems. Singapore: Springer, 2017, 2378\u20132386. DOI: 10.1007\/978-981-10-6571-2_290.<\/li>\n
  13. [13] I. Chaturvedi, E. Cambria, R. E. Welsch, and F. Herrera, (2018) \u201cDistinguishing between facts and opinions for sentiment analysis: Survey and challenges\u201d Information Fusion 44: 65\u201377. DOI: 10.1016\/j.inffus.2017.12.006.<\/li>\n
  14. [14] J. Wu, T. Zhu, X. Zheng, and C. Wang, (2022) \u201cMulti-modal sentiment analysis based on interactive attention mechanism\u201d Applied Sciences 12: 8174. DOI: 10.3390\/app12168174.<\/li>\n
  15. [15] M. G. Huddar, S. S. Sannakki, and V. S. Rajpurohit, (2021) \u201cAttention-based multi-modal sentiment analysis and emotion detection in conversation using RNN\u201d International Journal of Interactive Multimedia and Artificial Intelligence. DOI: 10.9781\/ijimai.2020.07.004.<\/li>\n
  16. [16] W. Yuzhu, X. Jun, C. Bo, and X. Xinying, (2021) \u201cMulti-modal sentiment analysis based on cross-modal context-aware attention\u201d Data Analysis and Knowledge Discovery 1: DOI: 10.11925\/infotech.2096-3467.2020.1042.<\/li>\n
  17. [17] J. Zhang, Z. Yin, P. Chen, and S. Nichele, (2020) \u201cEmotion recognition using multi-modal data and machine learning techniques: A tutorial and review\u201d Information Fusion 59: 103\u2013126. DOI: 10.1016\/j.inffus.2020.01.011.<\/li>\n
  18. [18] A. Kumar and J. Vepa. \u201cGated mechanism for attention based multi modal sentiment analysis\u201d. In: ICASSP 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2020, 4477\u20134481. DOI: 10.1109\/ICASSP40776.2020.953012.<\/li>\n
  19. [19] D. H. Kim, M. K. Lee, D. Y. Choi, and B. C. Song. \u201cMulti-modal emotion recognition using semi-supervised learning and multiple neural networks in the wild\u201d. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction. ACM, 2017, 529\u2013535. DOI: 10.1145\/3136755.3143005.<\/li>\n
  20. [20] S. Latif, H. Cuay\u00e1huitl, F. Pervez, F. Shamshad, H. S. Ali, and E. Cambria, (2022) \u201cA survey on deep reinforcement learning for audio-based applications\u201d Artificial Intelligence Review: 1\u201348. DOI: 10.1007\/s10462-022-10224-2.<\/li>\n
  21. [21] M. Sivakumara and S. R. Uyyalab, (2022) \u201cAspect-based sentiment analysis of product reviews using multi-agent deep reinforcement learning\u201d Asia Pacific Journal of Information Systems 32: 226\u2013248. DOI: 10.14329\/apjis.2022.32.2.226.<\/li>\n
  22. [22] S. J. Park, D. K. Chae, H. K. Bae, S. Park, and S. W. Kim. \u201cReinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation\u201d. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining. ACM, 2022, 784\u2013793. DOI: 10.1145\/3488560.3498515.<\/li>\n
  23. [23] F. Nadeem. \u201cMulti-modal reinforcement learning with videogame audio to learn sonic features\u201d. (phdthesis). Massachusetts Institute of Technology, 2020.<\/li>\n
  24. [24] E. Acar, F. Hopfgartner, and S. Albayrak. \u201cFusion of learned multi-modal representations and dense trajectories for emotional analysis in videos\u201d. In: 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2015, 1\u20136. DOI: 10.1109\/CBMI.2015.7153603.<\/li>\n
  25. [25] B. Schuller, F. Weninger, and J. Dorfner. \u201cMulti-modal non-prototypical music mood analysis in continuous space: reliability and performances\u201d. In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR). 2011.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,720,6],"tags":[1448],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202609_32.039\u00a0\u00a0 Download PDF The study of intelligent computing and deep learning has become a…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/6879"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6879"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6879"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6879"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}