{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:00:53Z","timestamp":1754154053249,"version":"3.41.2"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819500192"},{"type":"electronic","value":"9789819500208"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0020-8_19","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T09:18:13Z","timestamp":1753262293000},"page":"224-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Emotion Prediction Using Instruction-Tuned Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2967-1579","authenticated-orcid":false,"given":"Bin","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5321-7841","authenticated-orcid":false,"given":"Yujuan","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6286-8538","authenticated-orcid":false,"given":"Wen","family":"Shang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1147-5791","authenticated-orcid":false,"given":"Yuzhen","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1434-0977","authenticated-orcid":false,"given":"Yanjie","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8181-2735","authenticated-orcid":false,"given":"Min","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0533-9359","authenticated-orcid":false,"given":"Tinghu","family":"Kang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"19_CR1","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. OpenAI Blog (2018). https:\/\/openai.com\/research\/language-unsupervised"},{"key":"19_CR2","unstructured":"Yang, H., et al.: Large language models meet text-centric multimodal sentiment analysis: a survey. arXiv preprint arXiv:2406.08068 (2024)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, W., Deng, Y., Liu, B., Pan, S.J., Bing, L.: Sentiment analysis in the era of large language models: a reality check. arXiv preprint arXiv:2305.15005 (2023)","DOI":"10.18653\/v1\/2024.findings-naacl.246"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yang, K., Xie, Q., Zhang, T., Ananiadou, S.: EmoLLMs: a series of emotional large language models and annotation tools for comprehensive affective analysis. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 5487\u20135496 (2024)","DOI":"10.1145\/3637528.3671552"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yang, H., Zhou, T., Babar, M.A., Liu, X.-Y.: Enhancing financial sentiment analysis via retrieval augmented large language models. In: Proceedings of the Fourth ACM International Conference on AI in Finance, pp. 349\u2013356 (2023)","DOI":"10.1145\/3604237.3626866"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Xiao, W.: A comparative review of advanced techniques for financial sentiment analysis. In: Proceedings of the International Conference on Modeling, Natural Language Processing, and Machine Learning, pp. 76\u201380 (2024)","DOI":"10.1145\/3677779.3677791"},{"key":"19_CR7","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2019)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Mir, J., Mahmood, A., Khatoon, S., Hussain, S., Ullah, S.S., Iqbal, J.: Application domains of aspect and sentiment classification techniques: a survey. Neurocomputing 622, 129237 (2025)","DOI":"10.1016\/j.neucom.2024.129237"},{"key":"19_CR9","unstructured":"Yang, A., et al.: Qwen2 technical report. arXiv preprint arXiv:2407.10671 (2024)"},{"key":"19_CR10","unstructured":"Li, C., et al.: Large language models understand and can be enhanced by emotional stimuli. arXiv preprint arXiv:2307.11760 (2023)"},{"key":"19_CR11","unstructured":"Yang, A., et al.: Baichuan 2: open large-scale language models. arXiv preprint arXiv:2309.10305, (2023)"},{"key":"19_CR12","unstructured":"GLM Team, et al.: ChatGLM: a family of large language models from GLM-130B to GLM-4 all tools. arXiv preprint arXiv:2406.12793 (2024)"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Reimers, N.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084, (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, J.M., Penichet, V.M.R., Lozano, M.D.: Emotion detection: a technology review. In: Proceedings of the XVIII International Conference on Human Computer Interaction, pp. 1\u20138 (2017)","DOI":"10.1145\/3123818.3123852"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Al Maruf, A., Khanam, F., Haque, M.M., Jiyad, Z.M., Mridha, F., Aung, Z.: Challenges and opportunities of text-based emotion detection: a survey. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3356357"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Ahmad, G.I., Singla, J., Anis, A., Reshi, A.A., Salameh, A.A.: Machine learning techniques for sentiment analysis of code-mixed and switched Indian social media text corpus - a comprehensive review. Int. J. Adv. Comput. Sci. Appl. 13(2) (2022). https:\/\/doi.org\/10.14569\/IJACSA.2022.0130235","DOI":"10.14569\/IJACSA.2022.0130235"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Nandwani, P., Verma, R.: A review on sentiment analysis and emotion detection from text. Soc. Netw. Anal. Mining 11(1), 81 (2021)","DOI":"10.1007\/s13278-021-00776-6"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Wankhade, M., Rao, A.C.S., Kulkarni, C.: A survey on sentiment analysis methods, applications, and challenges. Artif. Intell. Rev. 55(7), 5731\u20135780 (2022)","DOI":"10.1007\/s10462-022-10144-1"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"W\u00f3\u017aniak, S., Koptyra, B., Janz, A., Kazienko, P., Koco\u0144, J.: Personalized large language models. arXiv preprint arXiv:2402.09269 (2024)","DOI":"10.1109\/ICDMW65004.2024.00071"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: CPED: a large-scale Chinese personalized and emotional dialogue dataset for conversational AI. arXiv preprint arXiv:2205.14727 (2022)","DOI":"10.36227\/techrxiv.19919483"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yang, H., Zhou, T., Ali Babar, M., Liu, X.-Y.: Enhancing financial sentiment analysis via retrieval augmented large language models. In: Proceedings of the Fourth ACM International Conference on AI in Finance, pp. 349\u2013356 (2023)","DOI":"10.1145\/3604237.3626866"},{"key":"19_CR22","first-page":"1950","volume":"35","author":"H Liu","year":"2022","unstructured":"Liu, H., et al.: Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Adv. Neural. Inf. Process. Syst. 35, 1950\u20131965 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR23","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR24","unstructured":"Hu, E. J., et al.: LoRA: low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"19_CR25","unstructured":"Zhao, W. X., et al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Harnessing the power of LLMs in practice: a survey on chatGPT and beyond. ACM Trans. Knowl. Disc. Data 18(6), 1\u201332 (2024)","DOI":"10.1145\/3649506"},{"key":"19_CR27","unstructured":"Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Kosti, R., Alvarez, J.M., Recasens, A., Lapedriza, A.: Emotion recognition in context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1667\u20131675 (2017)","DOI":"10.1109\/CVPR.2017.212"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0020-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T22:12:44Z","timestamp":1753308764000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0020-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500192","9789819500208"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0020-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}