{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:05:31Z","timestamp":1773936331476,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72201212"],"award-info":[{"award-number":["72201212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Stat Comput"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Multi-brand analysis based on review comments and ratings is a commonly used strategy to compare different brands in marketing. It can help consumers make more informed decisions and help marketers understand their brand\u2019s position in the market. In this work, we propose a multifacet hierarchical sentiment-topic model (MH-STM) to detect brand-associated sentiment polarities towards multiple comparative aspects from online customer reviews. The proposed method is built on a unified generative framework that explains review words with a hierarchical brand-associated topic model and the overall polarity score with a regression model on the empirical topic distribution. Moreover, a novel hierarchical P\u00f3lya urn (HPU) scheme is proposed to enhance the topic-word association among topic hierarchy, such that the general topics shared by all brands are separated effectively from the unique topics specific to individual brands. The performance of the proposed method is evaluated on both synthetic data and two real-world review corpora. Experimental studies demonstrate that the proposed method can be effective in detecting reasonable topic hierarchy and deriving accurate brand-associated rankings on multi-aspects.<\/jats:p>","DOI":"10.1007\/s11222-025-10593-y","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T06:12:56Z","timestamp":1741155176000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A multifacet hierarchical sentiment-topic model with application to multi-brand online review analysis"],"prefix":"10.1007","volume":"35","author":[{"given":"Qiao","family":"Liang","sequence":"first","affiliation":[]},{"given":"Xinwei","family":"Deng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"10593_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2022.102989","volume":"67","author":"M Alzate","year":"2022","unstructured":"Alzate, M., Arce-Urriza, M., Cebollada, J.: Mining the text of online consumer reviews to analyze brand image and brand positioning. J. Retail. Consum. Serv. 67, 102989 (2022)","journal-title":"J. Retail. Consum. Serv."},{"issue":"2","key":"10593_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1667053.1667056","volume":"57","author":"DM Blei","year":"2010","unstructured":"Blei, D.M., Griffiths, T.L., Jordan, M.I.: The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57(2), 1\u201330 (2010)","journal-title":"J. ACM"},{"key":"10593_CR3","unstructured":"Boyd-Graber, J., Resnik, P.: Holistic sentiment analysis across languages: Multilingual supervised latent dirichlet allocation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 45\u201355. Association for Computational Linguistics, USA (2010)"},{"key":"10593_CR4","unstructured":"Blei, D.M., Jordan, M.I., Griffiths, T.L., Tenenbaum, J.B.: Hierarchical topic models and the nested chinese restaurant process. In: Proceedings of the 16th International Conference on Neural Information Processing Systems, pp. 17\u201324. MIT Press, Cambridge, MA, USA (2003)"},{"issue":"3","key":"10593_CR5","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s12525-022-00543-1","volume":"32","author":"BM Brand","year":"2022","unstructured":"Brand, B.M., Kopplin, C.S., Rausch, T.M.: Cultural differences in processing online customer reviews: holistic versus analytic thinkers. Electron. Mark. 32(3), 1039\u20131060 (2022)","journal-title":"Electron. Mark."},{"key":"10593_CR6","unstructured":"Blei, D.M., McAuliffe, J.D.: Supervised topic models. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, pp. 121\u2013128. Curran Associates Inc., Red Hook, NY, USA (2007)"},{"issue":"Jan","key":"10593_CR7","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"10593_CR8","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1080\/19325037.2018.1473180","volume":"49","author":"AE Barry","year":"2018","unstructured":"Barry, A.E., Valdez, D., Padon, A.A., Russell, A.M.: Alcohol advertising on twitter-a topic model. Am. J. Health Educ. 49(4), 256\u2013263 (2018)","journal-title":"Am. J. Health Educ."},{"key":"10593_CR9","unstructured":"Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., Blei, D.M.: Reading tea leaves: How humans interpret topic models. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems, pp. 288\u2013296. Curran Associates Inc., Red Hook, NY, USA (2009)"},{"key":"10593_CR10","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.jbusres.2018.03.026","volume":"88","author":"AF Colladon","year":"2018","unstructured":"Colladon, A.F.: The semantic brand score. J. Bus. Res. 88, 150\u2013160 (2018)","journal-title":"J. Bus. Res."},{"issue":"7","key":"10593_CR11","doi-asserted-by":"publisher","first-page":"826","DOI":"10.14778\/3192965.3192972","volume":"11","author":"J Chen","year":"2018","unstructured":"Chen, J., Zhu, J., Lu, J., Liu, S.: Scalable training of hierarchical topic models. Proc. VLDB Endow. 11(7), 826\u2013839 (2018)","journal-title":"Proc. VLDB Endow."},{"key":"10593_CR12","doi-asserted-by":"crossref","unstructured":"Doyle, G., Elkan, C.: Accounting for burstiness in topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 281\u2013288 (2009)","DOI":"10.1145\/1553374.1553410"},{"issue":"4","key":"10593_CR13","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s10660-022-09534-y","volume":"23","author":"Z He","year":"2023","unstructured":"He, Z., Zheng, L., He, S.: A novel approach for product competitive analysis based on online reviews. Electron. Commer. Res. 23(4), 2259\u20132290 (2023)","journal-title":"Electron. Commer. Res."},{"key":"10593_CR14","unstructured":"Jagarlamudi, J., Daum\u00e9\u00a0III, H., Udupa, R.: Incorporating lexical priors into topic models. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 204\u2013213 (2012)"},{"key":"10593_CR15","doi-asserted-by":"crossref","unstructured":"Kang, J.-H., Ma, J., Liu, Y.: Transfer topic modeling with ease and scalability. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 564\u2013575 (2012). SIAM","DOI":"10.1137\/1.9781611972825.49"},{"key":"10593_CR16","doi-asserted-by":"crossref","unstructured":"Kim, S., Zhang, J., Chen, Z., Oh, A., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 526\u2013533 (2013)","DOI":"10.1609\/aaai.v27i1.8700"},{"issue":"8","key":"10593_CR17","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1080\/24725854.2023.2228861","volume":"56","author":"Q Liang","year":"2024","unstructured":"Liang, Q.: Tree-based data filtering for online user-generated reviews. IISE Trans. 56(8), 824\u2013840 (2024). https:\/\/doi.org\/10.1080\/24725854.2023.2228861","journal-title":"IISE Trans."},{"key":"10593_CR18","volume-title":"Matching Theory","author":"L Lov\u00e1sz","year":"2009","unstructured":"Lov\u00e1sz, L., Plummer, M.D.: Matching Theory. American Mathematical Soc, Providence, RI (2009)"},{"issue":"1","key":"10593_CR19","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1080\/00401706.2022.2063187","volume":"65","author":"Q Liang","year":"2023","unstructured":"Liang, Q., Ranganathan, S., Wang, K., Deng, X.: JST-RR model: Joint modeling of ratings and reviews in sentiment-topic prediction. Technometrics 65(1), 57\u201369 (2023). https:\/\/doi.org\/10.1080\/00401706.2022.2063187","journal-title":"Technometrics"},{"key":"10593_CR20","doi-asserted-by":"crossref","unstructured":"Liu, L., Tang, L., He, L., Zhou, W., Yao, S.: An overview of hierarchical topic modeling. In: 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 391\u2013394 (2016). IEEE","DOI":"10.1109\/IHMSC.2016.101"},{"key":"10593_CR21","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, H., Zhang, Z., Sun, A., Ma, Z.: Topic modeling for short texts with auxiliary word embeddings. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165\u2013174 (2016)","DOI":"10.1145\/2911451.2911499"},{"key":"10593_CR22","doi-asserted-by":"publisher","DOI":"10.1201\/9781420059847","volume-title":"P\u00f3lya Urn Models","author":"H Mahmoud","year":"2008","unstructured":"Mahmoud, H.: P\u00f3lya Urn Models. Chapman and Hall\/CRC, Boca Raton, FL (2008)"},{"key":"10593_CR23","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.jbusres.2020.04.003","volume":"114","author":"S Mitra","year":"2020","unstructured":"Mitra, S., Jenamani, M.: Obim: a computational model to estimate brand image from online consumer review. J. Bus. Res. 114, 213\u2013226 (2020)","journal-title":"J. Bus. Res."},{"key":"10593_CR24","doi-asserted-by":"crossref","unstructured":"Madsen, R.E., Kauchak, D., Elkan, C.: Modeling word burstiness using the dirichlet distribution. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 545\u2013552 (2005)","DOI":"10.1145\/1102351.1102420"},{"key":"10593_CR25","doi-asserted-by":"crossref","unstructured":"McAuley, J., Leskovec, J., Jurafsky, D.: Learning attitudes and attributes from multi-aspect reviews. In: 2012 IEEE 12th International Conference on Data Mining, pp. 1020\u20131025 (2012). IEEE","DOI":"10.1109\/ICDM.2012.110"},{"key":"10593_CR26","doi-asserted-by":"crossref","unstructured":"Mimno, D., Li, W., McCallum, A.: Mixtures of hierarchical topics with pachinko allocation. In: Proceedings of the 24th International Conference on Machine Learning, pp. 633\u2013640 (2007)","DOI":"10.1145\/1273496.1273576"},{"key":"10593_CR27","unstructured":"Mimno, D., Wallach, H., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 262\u2013272 (2011)"},{"key":"10593_CR28","unstructured":"Nguyen, V.-A., Boyd-Graber, J., Resnik, P.: Lexical and hierarchical topic regression. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 1106\u20131114. Curran Associates Inc., Red Hook, NY, USA (2013)"},{"key":"10593_CR29","doi-asserted-by":"crossref","unstructured":"Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 188\u2013197 (2019)","DOI":"10.18653\/v1\/D19-1018"},{"issue":"4","key":"10593_CR30","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1002\/joe.22153","volume":"41","author":"S Sajid","year":"2022","unstructured":"Sajid, S., Volkova, N., Wilson, J.A., Opoku-Asante, E.: Using text mining and crowdsourcing platforms to build employer brand in the us banking industry. Glob. Bus. Organ. Excell. 41(4), 6\u201327 (2022)","journal-title":"Glob. Bus. Organ. Excell."},{"key":"10593_CR31","doi-asserted-by":"crossref","unstructured":"Tumpa, S.N., Ali, M.M.: Document concept hierarchy generation by extracting semantic tree using knowledge graph. In: 2018 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 83\u201386 (2018). IEEE","DOI":"10.1109\/WIECON-ECE.2018.8783083"},{"key":"10593_CR32","doi-asserted-by":"crossref","unstructured":"Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111\u2013120 (2008)","DOI":"10.1145\/1367497.1367513"},{"key":"10593_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2020.101582","volume":"94","author":"I Vayansky","year":"2020","unstructured":"Vayansky, I., Kumar, S.A.: A review of topic modeling methods. Inform. Syst. 94, 101582 (2020)","journal-title":"Inform. Syst."},{"key":"10593_CR34","doi-asserted-by":"crossref","unstructured":"Vafa, K., Naidu, S., Blei, D.: Text-based ideal points. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5345\u20135357 (2020)","DOI":"10.18653\/v1\/2020.acl-main.475"},{"key":"10593_CR35","doi-asserted-by":"crossref","unstructured":"Wallach, H.M., Murray, I., Salakhutdinov, R., Mimno, D.: Evaluation methods for topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1105\u20131112 (2009)","DOI":"10.1145\/1553374.1553515"},{"key":"10593_CR36","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.neucom.2019.12.013","volume":"385","author":"R Wang","year":"2020","unstructured":"Wang, R., Zhou, D., He, Y.: Optimising topic coherence with weighted polya urn scheme. Neurocomputing 385, 329\u2013339 (2020)","journal-title":"Neurocomputing"},{"key":"10593_CR37","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.eswa.2018.03.008","volume":"103","author":"Y Xu","year":"2018","unstructured":"Xu, Y., Yin, J., Huang, J., Yin, Y.: Hierarchical topic modeling with automatic knowledge mining. Expert Syst. Appl. 103, 106\u2013117 (2018)","journal-title":"Expert Syst. Appl."},{"key":"10593_CR38","doi-asserted-by":"crossref","unstructured":"Zhao, R., Gui, L., Pergola, G., He, Y.: Adversarial learning of poisson factorisation model for gauging brand sentiment in user reviews. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 2341\u20132351 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.199"},{"key":"10593_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, R., Gui, L., Yan, H., He, Y.: Tracking brand-associated polarity-bearing topics in user reviews. Trans. Assoc. Comput. Linguistics 11, 404\u2013418 (2023)","DOI":"10.1162\/tacl_a_00555"},{"key":"10593_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, H., Kim, G., Xing, E.P.: Dynamic topic modeling for monitoring market competition from online text and image data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1425\u20131434 (2015)","DOI":"10.1145\/2783258.2783293"},{"issue":"7","key":"10593_CR41","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1108\/OIR-01-2019-0037","volume":"43","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Tong, T., Bu, Y.: Examining differences among book reviews from various online platforms. Online Inform. Rev. 43(7), 1169\u20131187 (2019)","journal-title":"Online Inform. Rev."},{"key":"10593_CR42","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s10115-015-0882-z","volume":"48","author":"Y Zuo","year":"2016","unstructured":"Zuo, Y., Zhao, J., Xu, K.: Word network topic model: a simple but general solution for short and imbalanced texts. Knowl. Inf. Syst. 48, 379\u2013398 (2016)","journal-title":"Knowl. Inf. Syst."}],"container-title":["Statistics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-025-10593-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11222-025-10593-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-025-10593-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T16:59:40Z","timestamp":1745945980000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11222-025-10593-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,5]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10593"],"URL":"https:\/\/doi.org\/10.1007\/s11222-025-10593-y","relation":{},"ISSN":["0960-3174","1573-1375"],"issn-type":[{"value":"0960-3174","type":"print"},{"value":"1573-1375","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,5]]},"assertion":[{"value":"30 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"62"}}