{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:40:56Z","timestamp":1776372056742,"version":"3.51.2"},"reference-count":28,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan Key R&amp;D and Promotion Project (Soft Science)","award":["242400411171"],"award-info":[{"award-number":["242400411171"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as \u201ccamera performance\u201d, \u201coperational speed\u201d, \u201cscreen quality\u201d, and \u201cbattery life\u201d. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand\u2019s cell phones, which provides a quantitative basis for enterprises to understand users\u2019 emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries.<\/jats:p>","DOI":"10.3390\/info16070614","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T14:13:43Z","timestamp":1752761623000},"page":"614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multilayer Network Modeling for Brand Knowledge Discovery: Integrating TF-IDF and TextRank in Heterogeneous Semantic Space"],"prefix":"10.3390","volume":"16","author":[{"given":"Peng","family":"Xu","sequence":"first","affiliation":[{"name":"School of Humanities, Jiaozuo University, Jiaozuo 454000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rixu","family":"Zang","sequence":"additional","affiliation":[{"name":"Agricultural Bank of China Limited Weifang Branch, Weifang 261000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6898-9351","authenticated-orcid":false,"given":"Zongshui","family":"Wang","sequence":"additional","affiliation":[{"name":"Business School, Beijing Information Science and Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0386-6809","authenticated-orcid":false,"given":"Zhuo","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Management, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maulidi, A. (2025). Cost efficiency and green product innovation in SMEs for emerging economies: The roles of green Brand knowledge and green innovation capability. J. Clean. Prod., 498.","DOI":"10.1016\/j.jclepro.2025.145130"},{"key":"ref_2","first-page":"837","article-title":"Effects of brand knowledge on green trust and green brand equity: Multigroup comparisons based on perceived brand greenness and age","volume":"28","author":"Tracie","year":"2024","journal-title":"J. Fash. Mark. Manag. Int. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, B., Tao, Z., Lin, S., and Li, H. (2025). Multi-level feature fusion parallel branching networks for point cloud learning. Comput. Graph., 128.","DOI":"10.1016\/j.cag.2025.104221"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Comas, S.D., Meschino, J.G., and Ballarin, L.V. (2025). Interval-valued fuzzy predicates from labeled data: An approach to data classification and knowledge discovery. Inf. Sci., 707.","DOI":"10.1016\/j.ins.2025.122033"},{"key":"ref_5","first-page":"37","article-title":"From Data Mining to Knowledge Discovery in Databases","volume":"17","author":"Fayyad","year":"1996","journal-title":"Ai Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1108\/JPBM-03-2024-5022","article-title":"A typology of brand knowledge associations projected in brand-generated signals","volume":"34","author":"Cleopatra","year":"2025","journal-title":"J. Prod. Brand Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"93","DOI":"10.4018\/JITR.2019070106","article-title":"Sentiment analysis of brand personality positioning through text mining","volume":"12","author":"Lu","year":"2019","journal-title":"J. Inf. Technol. Res. (JITR)"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Joyce, B., and Deng, J. (2019, January 9\u201313). Sentiment Analysis Using Naive Bayes Approach with Weighted Reviews-A Case Study. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013588"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s11192-018-2989-y","article-title":"Frontier knowledge discovery and visualization in cancer field based on KOS and LDA","volume":"118","author":"Wu","year":"2019","journal-title":"Scientometrics"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, C., Xie, Z., and Wang, H. (2025). Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks. Appl. Sci., 15.","DOI":"10.3390\/app15095102"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10559-025-00772-0","article-title":"Comparative Analysis of Neural Network Models for Text Classification Problems","volume":"61","author":"Anisimov","year":"2025","journal-title":"Cybern. Syst. Anal."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y. (2024). Research on the TF\u2013IDF algorithm combined with semantics for automatic extraction of keywords from network news texts. J. Intell. Syst., 33.","DOI":"10.1515\/jisys-2023-0300"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, W., Sun, Y., Yu, B., Wang, H., Peng, Q., Hou, M., Guo, H., Wang, H., and Liu, C. (2024). Automatic Text Summarization Method Based on Improved TextRank Algorithm and K-Means Clustering. Knowl.-Based Syst., 287.","DOI":"10.1016\/j.knosys.2024.111447"},{"key":"ref_14","first-page":"17","article-title":"On the evolution of random graphs","volume":"5","year":"1960","journal-title":"Am. J. Ind. Bus. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u2018small-world\u2019 networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Albert","year":"1999","journal-title":"Science"},{"key":"ref_17","first-page":"106","article-title":"The Structure and Function of Multilayer Networks: Progress and Prospects","volume":"50","author":"Wu","year":"2021","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, K., and Li, H. (2022, January 26\u201329). Key Nodes Mining in Complex Networks Based on Improved Pagerank Algorithm. Proceedings of the 2022 IEEE\/ACIS 22nd International Conference on Computer and Information Science (ICIS), Zhuhai, China.","DOI":"10.1109\/ICIS54925.2022.9882507"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1093\/comnet\/cnx050","article-title":"Centralities of nodes and influences of layers in large multiplex networks","volume":"6","author":"Rahmede","year":"2018","journal-title":"J. Complex Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Interdonato, R., Magnani, M., Perna, D., Tagarelli, A., and Vega, D. (2020). Multilayer network simplification: Approaches, models and methods. Comput. Sci. Rev., 36.","DOI":"10.1016\/j.cosrev.2020.100246"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yablochnikov, S., Kuptsov, M., and Mahiboroda, M. (2021). Modeling of Information Processes in Social Networks. Information, 12.","DOI":"10.3390\/info12030116"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mikkilineni, R. (2022). A New Class of Autopoietic and Cognitive Machines. Information, 13.","DOI":"10.3390\/info13010024"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wu, Z., and Shi, Z. (2022, January 6\u20138). An improved algorithm of TFIDF combined with Naive Bayes. Proceedings of the 2022 7th International Conference on Multimedia and Image Processing, Xian, China.","DOI":"10.1145\/3517077.3517104"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rui, Z., and Yutai, H. (2020, January 20\u201322). Research on Short Text Classification Based on Word2Vec Microblog. Proceedings of the International Conference on Computer Science and Management Technology (ICCSMT), Shanghai, China.","DOI":"10.1109\/ICCSMT51754.2020.00042"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zixuan, C., and Shunli, G. (2022). Automatic Text Summarization for Public Health WeChat Official Accounts Platform Base on Improved TextRank. J. Environ. Public Health, 2022.","DOI":"10.1155\/2022\/1166989"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1142\/S0218127402004802","article-title":"Complex networks: Topology, dynamics and synchronization","volume":"12","author":"Wang","year":"2002","journal-title":"Int. J. Bifurc. Chaos"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0378-8733(78)90021-7","article-title":"Centrality in social networks conceptual clarification","volume":"1","author":"Freeman","year":"1978","journal-title":"Soc. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/0022250X.1972.9989806","article-title":"Factoring and weighting approaches to status scores and clique identification","volume":"2","author":"Bonacich","year":"1972","journal-title":"J. Math. Sociol."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/614\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:11:34Z","timestamp":1760033494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/614"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":28,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070614"],"URL":"https:\/\/doi.org\/10.3390\/info16070614","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]}}}