{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:36:02Z","timestamp":1760240162084,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,24]],"date-time":"2019-03-24T00:00:00Z","timestamp":1553385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013106","name":"Shanghai Science International Cooperation Project","doi-asserted-by":"publisher","award":["16550720400"],"award-info":[{"award-number":["16550720400"]}],"id":[{"id":"10.13039\/501100013106","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["91746203"],"award-info":[{"award-number":["91746203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Nowadays, massive texts are generated on the web, which contain a variety of viewpoints, attitudes, and emotions for products and services. Subjective information mining of online comments is vital for enterprises to improve their products or services and for consumers to make purchase decisions. Various effective methods, the mainstream one of which is the topic model, have been put forward to solve this problem. Although most of topic models can mine the topic-level emotion of the product comments, they do not consider interword relations and the number of topics determined adaptively, which leads to poor comprehensibility, high time requirement, and low accuracy. To solve the above problems, this paper proposes an unsupervised Topic-Specific Emotion Mining Model (TSEM), which adds corresponding relationship between aspect words and opinion words to express comments as a bag of aspect\u2013opinion pairs. On one hand, the rich semantic information obtained by adding interword relationship can enhance the comprehensibility of results. On the other hand, text dimensions reduced by adding relationships can cut the computation time. In addition, the number of topics in our model is adaptively determined by calculating perplexity to improve the emotion accuracy of the topic level. Our experiments using Taobao commodity comments achieve better results than baseline models in terms of accuracy, computation time, and comprehensibility. Therefore, our proposed model can be effectively applied to online comment emotion mining tasks.<\/jats:p>","DOI":"10.3390\/fi11030079","type":"journal-article","created":{"date-parts":[[2019,3,25]],"date-time":"2019-03-25T06:56:52Z","timestamp":1553497012000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Topic-Specific Emotion Mining Model for Online Comments"],"prefix":"10.3390","volume":"11","author":[{"given":"Xiangfeng","family":"Luo","sequence":"first","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Yawen","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,24]]},"reference":[{"key":"ref_1","first-page":"269","article-title":"Actual Situation and Development in Online Shopping in the Czech Republic, Visegrad Group and EU-28","volume":"Volume 769","year":"2018","journal-title":"Modern Approaches for Intelligent Information and Database Systems"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.elerap.2018.03.008","article-title":"Ranking Online Consumer Reviews","volume":"29","author":"Saumya","year":"2018","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJEBR.2017070103","article-title":"Online Product Review, Product Knowledge, Attitude, and Online Purchase Behavior","volume":"13","author":"Yap","year":"2017","journal-title":"Int. J. E-Bus. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.eswa.2016.03.028","article-title":"Classification of Sentiment Reviews using n-Gram Machine Learning Approach","volume":"57","author":"Tripathy","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_5","unstructured":"Wan, Y., Nie, H., Lan, T., and Wang, Z. (2015, January 15\u201317). Fine-grained Sentiment Analysis of Online Reviews. Proceedings of the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10660-017-9279-2","article-title":"Product Innovation Based on online Review Data Mining: A Case Study of Huawei Phones","volume":"18","author":"Zhang","year":"2018","journal-title":"Electron. Commer. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.is.2012.03.001","article-title":"Improving the Quality of Predictions using Textual Information in Online User Reviews","volume":"38","author":"Ganu","year":"2013","journal-title":"Inf. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MIS.2009.105","article-title":"A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews","volume":"25","author":"Dang","year":"2010","journal-title":"IEEE Intell. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6527","DOI":"10.1016\/j.eswa.2008.07.035","article-title":"Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches","volume":"36","author":"Ye","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_10","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, C., and He, Y. (2009, January 2\u20136). Joint Sentiment\/Topic Model for Sentiment Analysis. Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong, China.","DOI":"10.1145\/1645953.1646003"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jo, Y., and Oh, A.H. (2011, January 9\u201312). Aspect and Sentiment Unification Model for Online Review Analysis. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, Hong Kong, China.","DOI":"10.1145\/1935826.1935932"},{"key":"ref_13","unstructured":"Azzopardi, L., Girolami, M., and Risjbergen, K.V. (August, January 28). Investigating the Relationship between Language Model Perplexity and IR Precision-recall Measures. Proceedings of the Annual ACM Conference on Research and Development in Information Retrieval, Toronto, ON, Canada."},{"key":"ref_14","first-page":"1739","article-title":"Sentiment Analysis on Movie Reviews Based on Combined Approach","volume":"3","author":"Mulkalwar","year":"2012","journal-title":"Int. J. Sci. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Augustyniak, L., Kajdanowicz, T., Kazienko, P., Kulisiewicz, M., and Tuliglowicz, W. (2014, January 11\u201313). An Approach to Sentiment Analysis of Movie Reviews: Lexicon Based vs. Classification. Proceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems, Salamanca, Spain.","DOI":"10.1007\/978-3-319-07617-1_15"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4018\/IJITWE.2016070102","article-title":"Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews","volume":"11","author":"Jararweh","year":"2016","journal-title":"Int. J. Inf. Technol. Web Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/j.1467-8640.2006.00277.x","article-title":"Sentiment Classification of Movie Reviews Using Contextual Valence Shifters","volume":"22","author":"Kennedy","year":"2006","journal-title":"Comput. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.joi.2009.01.003","article-title":"Sentiment Analysis: A Combined Approach","volume":"3","author":"Prabowo","year":"2009","journal-title":"J. Informetr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/T-AFFC.2011.2","article-title":"Aspect-Based Opinion Polling from Customer Reviews","volume":"2","author":"Zhu","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Poria, S., Cambria, E., Ku, L.-W., Gui, C., and Gelbukh, A. (2014, January 24). A Rule-based Approach to Aspect Extraction from Product Reviews. Proceedings of the Second Workshop on Natural Language Processing for Social Media, Dublin, Ireland.","DOI":"10.3115\/v1\/W14-5905"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Schouten, K., and Frasincar, F. (2014, January 1\u20134). Finding Implicit Features in Consumer Reviews for Sentiment Analysis. Proceedings of the International Conference on Web Engineering, Toulouse, France.","DOI":"10.1007\/978-3-319-08245-5_8"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jeyapriya, A., and Selvi, C.K. (2015, January 26\u201327). Extracting Aspects and Mining Opinions in Product Reviews using Supervised Learning Algorithm. Proceedings of the 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.","DOI":"10.1109\/ECS.2015.7124967"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bhoir, P., and Kolte, S. (2016, January 10\u201312). Sentiment Analysis of Movie Reviews using Lexicon Approach. Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India.","DOI":"10.1109\/ICCIC.2015.7435796"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mei, Q., Ling, X., Wondra, M., Su, H., and Zhai, C. (2007, January 8\u201312). Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs. Proceedings of the 16th International Conference on World Wide Web, Banff, AB, Canada.","DOI":"10.1145\/1242572.1242596"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1177\/0165551514538744","article-title":"ADM-LDA: An Aspect Detection Model Based on Topic Modelling using the Structure of Review Sentences","volume":"40","author":"Bagheri","year":"2014","journal-title":"J. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, Z., and Liu, B. (2016, January 11\u201315). Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model. Proceedings of the 25th International Conference on World Wide Web, Montr\u00e9al, QC, Canada.","DOI":"10.1145\/2872427.2883086"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Park, D.H., Zhai, C.X., and Guo, L. SpecLDA: Modeling Product Reviews and Specifications to Generate Augmented Specifications. Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada.","DOI":"10.1137\/1.9781611974010.94"},{"key":"ref_28","unstructured":"Griffiths, T. (2019, March 24). Gibbs Sampling in the generative model of Latent Dirichlet Allocation. Available online: https:\/\/people.cs.umass.edu\/~wallach\/courses\/s11\/cmpsci791ss\/readings\/griffiths02gibbs.pdf."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/3\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:40:20Z","timestamp":1760186420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/3\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,24]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["fi11030079"],"URL":"https:\/\/doi.org\/10.3390\/fi11030079","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2019,3,24]]}}}