Extracting Fine-Grained Sentiment Features about Library Services from Reader Feedback Text Using RoBERTa
Main Article Content
Abstract
Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa) model, fine-tuned for domain adaptation with library domain data, and introducing the Aspect-Based Sentiment Analysis (ABSA) framework to extract and classify fine-grained sentiment related to collection services in reader feedback. Based on annotation and experiments with 23,684 open reader feedback texts from 12 university libraries in China over the past three years, a fine-grained annotation system is constructed, encompassing six collection service dimensions, three types of sentiment polarity, including positive, neutral, and negative, and three levels of intensity. The results show that the domain-fine-tuned RoBERTa model achieved an F1 score of 86.7% in aspect recognition and an accuracy of 89.3% in sentiment polarity classification. The aspect recognition F1 score is significantly better than baseline models such as Bidirectional Encoder Representations from Transformers (BERT) by 5.2% and Text Convolutional Neural Network (TextCNN) by 11.6%. Among the evaluated dimensions, access to electronic resources and speed of obtaining books are the two dimensions with the highest concentration of negative sentiment, accounting for 31.4% and 27.8% of the total negative feedback, respectively. This research method improves the reliability and interpretability of sentiment analysis in professional service scenarios and provides a data-driven reference for optimizing digital resource services for engineering disciplines involving electromagnetic theory, antenna technology, and propagation research.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
O. Onunka, T. Onunka, A. A. Fawole, I. J. Adeleke, and C. Daraojimbae, “Library and information services in the digital age: Opportunities and challenges,” Acta Informatica Malaysia, vol. 7, no. 1, pp. 113-121, 2023, doi: 10.26480/aim.02.2023.113.121.
S. J. Jain and P. K. Behera, “Visualizing the academic library of the future based on collections, spaces, technologies, and services,” International Journal of Information Science & Management, vol. 21, no. 1, pp. 217-241, 2023, doi: 10.22034/ijism.2023.700794.
S. Kaur and R. Chakravarty, “Analytics for measuring library use and satisfaction of mobile apps,” Library Hi Tech News, vol. 38, no. 4, pp. 10-12, 2021, doi: 10.1108/LHTN-04-2021-0014.
Y. A. Ajani, E. K. Adefila, S. A. Olarongbe, R. T. Enakrire, and N. Rabiu, “Big data and the management of libraries in the era of the fourth industrial revolution: Implications for policymakers,” Digital Library Perspectives, vol. 40, no. 2, pp. 311-329, 2024, doi: 10.1108/DLP-10-2023-0083.
K. K. Twum, M. Adams, S. Budu, and R. A. Anati Budu, “Achieving university libraries user loyalty through user satisfaction: The role of service quality,” Journal of Marketing for Higher Education, vol. 32, no. 1, pp. 54-72, 2022, doi: 10.1080/08841241.2020.1825030.
C. Mubofu, “Experiences, purposes, satisfaction and missing library services as predictors of library information services provision: A case study,” Alexandria, vol. 33, no. 1, pp. 64-79, 2023, doi: 10.1177/095574902412447.
R. Miao, “Intelligent library service transformation strategies based on convolutional networks in the information age in the context of digital transformation,” Journal of Information & Knowledge Management, vol. 23, no. 2, Art. no. 2450028, 2024, doi: 10.1142/S021964922450028X.
V. J. H. Reji, “Digital transformation: Libraries implementing advanced IT solutions like AI and data analytics to improve services,” Academic Research Journal of Science and Technology, vol. 1, no. 7, pp. 1-13, 2025.
L. Ma, M. Li, W. N. Zhang, J. Li, and T. Liu, “Unstructured text enhanced open-domain dialogue system: A systematic survey,” ACM Transactions on Information Systems, vol. 40, no. 1, pp. 1-44, 2021, doi: 10.1145/3464377.
M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731-5780, 2022, doi: 10.1007/s10462-022-10144-1.
K. Alahmadi, S. Alharbi, J. Chen, and X. Wang, “Generalizing sentiment analysis: A review of progress, challenges, and emerging directions,” Social Network Analysis and Mining, vol. 15, no. 1, pp. 1-28, 2025, doi: 10.1007/s13278-025-01461-8.
M. Hu, H. Gao, Y. Wu, Z. Su, and S. Zhao, “Fine-grained domain adaptation for aspect category level sentiment analysis,” IEEE Transactions on Affective Computing, vol. 14, no. 4, pp. 2839-2850, 2022, doi: 10.1109/TAFFC.2022.3228695.
M. Bordoloi and S. K. Biswas, “Sentiment analysis: A survey on design framework, applications and future scopes,” Artificial Intelligence Review, vol. 56, no. 11, pp. 12505-12560, 2023, doi: 10.1007/s10462-023-10442-2.
S. K. Bichha, S. P. Singh, A. Choudhary, and K. Sahani, “Sentiment analysis of customer feedback: A pathway to emotion-centric service optimization,” Decision-Making, vol. 13, no. 14, pp. 15, 2025, [Online]. Available: https://www.jsiar.com/2025-April/JSIAR-A-25-04050.pdf.
S. A. Umezurike, O. V. Akinrinoye, O. T. Kufile, A. Y. Onifade, B. O. Otokiti, and O. G. Ejike, “Cross-platform sentiment analytics for unified customer feedback in digital business environments,” International Journal of Scientific Research in Science and Technology, vol. 12, no. 3, pp. 1224-1235, 2025, doi: 10.54660/.JFMR.2025.6.2.41-47.
O. Chamorro-Atalaya, L. Sobrino-Chunga, R. Guerrero-Carranza, A. Vargas-Díaz, and C. Poma-Garcia, “Student satisfaction in the context of hybrid learning through sentiment analysis,” International Journal of Evaluation and Research in Education, vol. 13, no. 2, pp. 831-841, 2024, doi: 10.11591/ijere.v13i2.26717.
S. Sarkar, “A comparative study of different natural language processing techniques for sentiment analysis and opinion mining,” Iconic Research and Engineering Journals, vol. 8, no. 12, pp. 1695-1708, 2025, [Online]. Available: https://www.irejournals.com/paper-details/1709305.
W. Li and M. Zhang, “Optimizing reading comprehension and understanding in learning by reading systems through natural language processing and user feedback,” Studies in Knowledge Discovery, Intelligent Systems, and Distributed Analytics, vol. 14, no. 11, pp. 26-39, 2024, [Online]. Available: https://edgescholar.com/index.php/SKDISDA/article/view/e-2024-11-10.
G. D’Aniello, M. Gaeta, and I. La Rocca, “KnowMIS-ABSA: An overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5543-5574, 2022, doi: 10.1007/s10462-021-10134-9.
M. M. Pakpahan, M. H. Dar, and M. N. S. Hasibuan, “Performance evaluation of machine learning algorithms in aspect-based sentiment analysis on e-commerce user reviews,” International Journal of Science, Technology & Management, vol. 6, no. 4, pp. 958-965, 2025, doi: 10.46729/ijstm.v6i4.1255.
R. Abdullah and R. Sarno, “Aspect based sentiment analysis for explicit and implicit aspects in restaurant review using grammatical rules, hybrid approach, and SentiCircle,” International Journal of Intelligent Engineering & Systems, vol. 14, no. 5, pp. 294-305, 2021, doi: 10.22266/ijies2021.1031.27.
A. Nazir, Y. Rao, L. Wu, and L. Sun, “Issues and challenges of aspect-based sentiment analysis: A comprehensive survey,” IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 845-863, 2020, doi: 10.1109/TAFFC.2020.2970399.
H. Liu, I. Chatterjee, M. Zhou, X. S. Lu, and A. Abusorrah, “Aspect-based sentiment analysis: A survey of deep learning methods,” IEEE Transactions on Computational Social Systems, vol. 7, no. 6, pp. 1358-1375, 2020, doi: 10.1109/TCSS.2020.3033302.
W. Liao, B. Zeng, X. Yin, and P. Wei, “An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa,” Applied Intelligence, vol. 51, no. 6, pp. 3522-3533, 2021, doi: 10.1007/s10489-020-01964-1.
J. Yan, P. Pu, and L. Jiang, “Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and graph neural networks,” PLOS ONE, vol. 20, no. 3, Art. no. e0318524, 2025, doi: 10.1371/journal.pone.0318524.
F. Alqarni, A. Sagheer, A. Alabbad, and H. Hamdoun, “Emotion-aware RoBERTa enhanced with emotion-specific attention and TF-IDF gating for fine-grained emotion recognition,” Scientific Reports, vol. 15, no. 1, Art. no. 17617, 2025, doi: 10.1038/s41598-025-99515-6.
H. Du, “Research on cloud storage biological data deduplication method based on Simhash algorithm,” International Journal of Data Mining and Bioinformatics, vol. 27, no. 4, pp. 252-266, 2023, doi: 10.1504/IJDMB.2023.134296.
S. Chaudhari, V. Mithal, G. Polatkan, and R. Ramanath, “An attentive survey of attention models,” ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 5, pp. 1-32, 2021, doi: 10.1145/3465055.
B. K. Nwobu and E. S. George, “Access to information resources using the online public access catalogue (OPAC) in the 21st century,” The Catalyst Journal of Library and Information Literacy, vol. 3, no. 1, pp. 140–153, 2024, [Online]. Available: https://journals.journalsplace.org/index.php/CJLIL/article/view/539.
J. Zeng, S. Cao, Y. Chen, P. Pan, and Y. Cai, “Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements,” Aslib Journal of Information Management, vol. 75, no. 3, pp. 589-617, 2023, doi: 10.1108/AJIM-03-2022-0130.
Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, et al., “Domain-specific language model pretraining for biomedical natural language processing,” ACM Transactions on Computing for Healthcare, vol. 3, no. 1, pp. 1-23, 2021, doi: 10.1145/3458754.
N. M. Gardazi, A. Daud, M. K. Malik, A. Bukhari, T. Alsahfi, and B. Alshemaimri, “BERT applications in natural language processing: A review,” Artificial Intelligence Review, vol. 58, no. 6, pp. 1-49, 2025, doi: 10.1007/s10462-025-11162-5.
J. Wang, J. X. Huang, X. Tu, J. Wang, A. J. Huang, M. T. R. Laskar, et al., “Utilizing BERT for information retrieval: Survey, applications, resources, and challenges,” ACM Computing Surveys, vol. 56, no. 7, pp. 1-33, 2024, doi: 10.1145/3648471.
V. K. Agbesi, W. Chen, S. B. Yussif, C. C. Ukwuoma, Y. H. Gu, and M. A. Al-antari, “MuTCELM: An optimal multi-TextCNNbased ensemble learning for text classification,” Heliyon, vol. 10, no. 19, Art. no. e38515, 2024, doi: 10.1016/j.heliyon.2024.e38515.
M. Wankhade, C. S. R. Annavarapu, and A. Abraham, “MAPA BiLSTM-BERT: Multi-aspects position aware attention for aspect level sentiment analysis,” Journal of Supercomputing, vol. 79, no. 10, Art. no. 11452, 2023, doi: 10.1007/s11227-023-05112-7.