{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:25:46Z","timestamp":1770884746591,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"government","award":["FSEG-2023-0008"],"award-info":[{"award-number":["FSEG-2023-0008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Topic modeling of large news streams is widely used to reconstruct economic and political narratives, which requires coherent topics with low lexical overlap while remaining interpretable to domain experts. We propose TF-SYN-NER-Rel, a structural\u2013semantic term weighting scheme that extends classical TF-IDF by integrating positional, syntactic, factual, and named-entity coefficients derived from morphosyntactic and dependency parses of Russian news texts. The method is embedded into a standard Latent Dirichlet Allocation (LDA) pipeline and evaluated on a large Russian-language news corpus from the online archive of Moskovsky Komsomolets (over 600,000 documents), with political, financial, and sports subsets obtained via dictionary-based expert labeling. For each subset, TF-SYN-NER-Rel is compared with standard TF-IDF under identical LDA settings, and topic quality is assessed using the C_v coherence metric. To assess robustness, we repeat model training across multiple random initializations and report aggregate coherence statistics. Quantitative results show that TF-SYN-NER-Rel improves coherence and yields smoother, more stable coherence curves across the number of topics. Qualitative analysis indicates reduced lexical overlap between topics and clearer separation of event-centered and institutional themes, especially in political and financial news. Overall, the proposed pipeline relies on CPU-based NLP tools and sparse linear algebra, providing a computationally lightweight and interpretable complement to embedding- and LLM-based topic modeling in large-scale news monitoring.<\/jats:p>","DOI":"10.3390\/info17010022","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:33:25Z","timestamp":1767191605000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Structural\u2013Semantic Term Weighting for Interpretable Topic Modeling with Higher Coherence and Lower Token Overlap"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1254-0464","authenticated-orcid":false,"given":"Dmitriy","family":"Rodionov","sequence":"first","affiliation":[{"name":"Graduate School of Economics and Engineering, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4685-8569","authenticated-orcid":false,"given":"Evgenii","family":"Konnikov","sequence":"additional","affiliation":[{"name":"Graduate School of Economics and Engineering, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0538-0942","authenticated-orcid":false,"given":"Gleb","family":"Golikov","sequence":"additional","affiliation":[{"name":"Research Laboratory \u201cPolytech-Invest\u201d, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6528-3896","authenticated-orcid":false,"given":"Polina","family":"Yakob","sequence":"additional","affiliation":[{"name":"Graduate School of Economics and Engineering, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1108\/eb026526","article-title":"A statistical interpretation of term specificity and its application in retrieval","volume":"28","year":"1972","journal-title":"J. Doc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-weighting approaches in automatic text retrieval","volume":"24","author":"Salton","year":"1988","journal-title":"Inf. Process. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10462-005-9001-y","article-title":"Evolving general term-weighting schemes for information retrieval: Tests on larger collections","volume":"24","author":"Cummins","year":"2005","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","unstructured":"Paik, J.H. (August, January 28). A novel TF-IDF weighting scheme for effective ranking. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201813), Dublin, Ireland."},{"key":"ref_5","first-page":"109","article-title":"Relation based term weighting regularization","volume":"Volume 7224","author":"Wu","year":"2012","journal-title":"Advances in Information Retrieval (ECIR 2012); Lecture Notes in Computer Science"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Arif, A.S.M., Rahman, M.M., and Mukta, S.Y. (2009, January 15\u201317). Information retrieval by modified term weighting method using random walk model with query term position ranking. Proceedings of the 2009 International Conference on Signal Processing Systems (ICSPS 2009), Singapore.","DOI":"10.1109\/ICSPS.2009.122"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Blanco, R., and Lioma, C. (2007, January 23\u201327). Random walk term weighting for information retrieval. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201807), Amsterdam, The Netherlands.","DOI":"10.1145\/1277741.1277930"},{"key":"ref_8","unstructured":"Marwah, D., and Beel, J. (2020, January 5). Term-recency for TF-IDF, BM25 and USE term weighting. Proceedings of the 8th International Workshop on Mining Scientific Publications (WOSP 2020), Wuhan, China. CEUR Workshop Proceedings."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.procs.2016.06.093","article-title":"Synonyms based term weighting scheme: An extension to TF.IDF","volume":"89","author":"Kumari","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lv, Y., and Zhai, C. (2009, January 19\u201323). Positional language models for information retrieval. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201809), Boston, MA, USA.","DOI":"10.1145\/1571941.1571994"},{"key":"ref_11","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","article-title":"Latent Dirichlet allocation (LDA) and topic modeling: Models, applications, a survey","volume":"78","author":"Jelodar","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/3373464.3373474","article-title":"A survey of multi-label topic models","volume":"21","author":"Burkhardt","year":"2019","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1177\/0165551515617393","article-title":"Topic modelling for qualitative studies","volume":"43","author":"Nikolenko","year":"2017","journal-title":"J. Inf. Sci."},{"key":"ref_15","unstructured":"Newman, D., Lau, J.H., Grieser, K., and Baldwin, T. (2010, January 14\u201316). Automatic evaluation of topic coherence. Proceedings of the 2010 Conference of the North American Chapter of the Association for Computational Linguistics\u2014Human Language Technologies (NAACL-HLT 2010), Los Angeles, CA, USA. Available online: https:\/\/aclanthology.org\/N10-1012."},{"key":"ref_16","unstructured":"Mimno, D., Wallach, H.M., Talley, E., Leenders, M., and McCallum, A. (2011, January 27\u201331). Optimizing semantic coherence in topic models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), Edinburgh, UK. Available online: https:\/\/aclanthology.org\/D11-1024."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"R\u00f6der, M., Both, A., and Hinneburg, A. (2015, January 2\u20136). Exploring the space of topic coherence measures. Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM 2015), Shanghai, China.","DOI":"10.1145\/2684822.2685324"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lau, J.H., Newman, D., and Baldwin, T. (2014, January 26\u201330). Machine Reading Tea Leaves: Automatically evaluating topic coherence and topic model quality. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), Gothenburg, Sweden.","DOI":"10.3115\/v1\/E14-1056"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fang, A., Macdonald, C., and Ounis, I. (2016, January 17\u201321). Using word embedding to evaluate the coherence of topics from Twitter data. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), Pisa, Italy.","DOI":"10.1145\/2911451.2914729"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Syed, S., and Spruit, M. (2017, January 19\u201321). Full-text or abstract? Examining topic coherence scores using Latent Dirichlet allocation. Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), Tokyo, Japan.","DOI":"10.1109\/DSAA.2017.61"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.1016\/j.eswa.2015.02.055","article-title":"An analysis of the coherence of descriptors in topic modeling","volume":"42","author":"Greene","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Doogan, C., and Buntine, W. (2021, January 6\u201311). Topic model or topic twaddle? Re-evaluating semantic interpretability measures. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2021), Online.","DOI":"10.18653\/v1\/2021.naacl-main.300"},{"key":"ref_23","first-page":"498","article-title":"How many topics? Stability analysis for topic models","volume":"Volume 8725","author":"Greene","year":"2014","journal-title":"Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014); Lecture Notes in Computer Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1177\/0165551519877049","article-title":"A bibliometric analysis of topic modeling studies (2000\u20132017)","volume":"47","author":"Li","year":"2021","journal-title":"J. Inf. Sci."},{"key":"ref_25","first-page":"e2181","article-title":"Topic coherence metrics: How sensitive are they?","volume":"13","author":"Campagnolo","year":"2022","journal-title":"J. Inf. Data Manag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Albalawi, R., Yeap, T.H., and Benyoucef, M. (2020). Using topic modeling methods for short-text data: A comparative analysis. Front. Artif. Intell., 3.","DOI":"10.3389\/frai.2020.00042"},{"key":"ref_27","unstructured":"Harrando, I., Lisena, P., and Troncy, R. (2021, January 6\u20137). Apples to apples: A systematic evaluation of topic models. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), Varna, Bulgaria."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1162\/tacl_a_00325","article-title":"Topic modeling in embedding spaces","volume":"8","author":"Dieng","year":"2020","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_29","unstructured":"Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv, Available online: https:\/\/arxiv.org\/abs\/2203.05794."},{"key":"ref_30","unstructured":"Nagda, M., and Fellenz, S. (2024, January 3\u20139). Putting back the stops: Integrating syntax with neural topic models. Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Jeju, Republic of Korea. Available online: https:\/\/www.ijcai.org\/proceedings\/2024\/0710.pdf."},{"key":"ref_31","unstructured":"Hoyle, A.M., Goel, P., Hian-Cheong, A., Peskov, D., Boyd-Graber, J., and Resnik, P. (2021, January 13\u201314). Is automated topic model evaluation broken? The incoherence of coherence. Proceedings of the NeurIPS Workshop on Robust AI in Text, Virtually. Available online: https:\/\/openreview.net\/pdf?id=tjdHCnPqoo."},{"key":"ref_32","unstructured":"Schroeder, K., and Wood-Doughty, Z. (2024). Reliability of Topic Modeling. arXiv, Available online: https:\/\/arxiv.org\/abs\/2410.23186."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Romero, J.D., Feijoo-Garcia, M.A., Nanda, G., Newell, B., and Magana, A.J. (2024). Evaluating the performance of topic modeling techniques with human validation to support qualitative analysis. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8100132"},{"key":"ref_34","unstructured":"Chen, H., Zhang, M., Li, J., Zhang, M., \u00d8vrelid, L., Haji\u010d, J., and Fei, H. (2025). Semantic role labeling: A systematical survey. arXiv, Available online: https:\/\/arxiv.org\/abs\/2502.08660."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, L., Lee, K., Lewis, M., and Zettlemoyer, L. (August, January 30). Deep semantic role labeling: What works and what\u2019s next. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, BC, Canada.","DOI":"10.18653\/v1\/P17-1044"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1162\/coli.2008.34.2.257","article-title":"The importance of syntactic parsing and inference in semantic role labeling","volume":"34","author":"Punyakanok","year":"2008","journal-title":"Comput. Linguist."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hassani, H., Beneki, C., Unger, S., Mazinani, M.T., and Yeganegi, M.R. (2020). Text mining in big data analytics. Big Data Cogn. Comput., 4.","DOI":"10.3390\/bdcc4010001"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, A., Chen, J., Yang, S.Y., and Hawkes, A.G. (2020). The flow of information in trading: An entropy approach to market regimes. Entropy, 22.","DOI":"10.3390\/e22091064"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rodionov, D., Zaytsev, A., Konnikov, E., Dmitriev, N., and Dubolazova, Y. (2021). Modeling changes in the enterprise information capital in the digital economy. J. Open Innov. Technol. Mark. Complex., 7.","DOI":"10.3390\/joitmc7030166"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Konnikov, E., Konnikova, O., Rodionov, D., and Yuldasheva, O. (2021). Analyzing natural digital information in the context of market research. Information, 12.","DOI":"10.3390\/info12100387"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rodionov, D., Lyamin, B., Konnikov, E., Obukhova, E., Golikov, G., and Polyakov, P. (2025). Integration of associative tokens into thematic hyperspace: A method for determining semantically significant clusters in dynamic text streams. Big Data Cogn. Comput., 9.","DOI":"10.3390\/bdcc9080197"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dmitriev, N., Zaytsev, A., and Konnikov, E. (2025, January 7\u201313). Graph-based model of semantic entanglement in information sources using embedding representations and coherence analysis. Proceedings of the 2025 International Russian Automation Conference (RusAutoCon), Sochi, Russia.","DOI":"10.1109\/RusAutoCon65989.2025.11177441"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zaytsev, A., Dmitriev, N., and Konnikov, E. (2025, January 7\u201313). Integration of event-driven modeling and stochastic optimization within control frameworks of regional energy systems. Proceedings of the 2025 International Russian Automation Conference (RusAutoCon), Sochi, Russia.","DOI":"10.1109\/RusAutoCon65989.2025.11177371"},{"key":"ref_44","first-page":"31","article-title":"Specification of regression analysis of the impact of the information environment on the financial performance of a company","volume":"3","author":"Konnikov","year":"2025","journal-title":"Softw. Syst. Comput. Methods"},{"key":"ref_45","first-page":"98","article-title":"Institutional structure of narratives in economics: A sociological approach to modeling socio-economic systems","volume":"4","author":"Tanova","year":"2024","journal-title":"Financ. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shiller, R.J. (2019). Narrative Economics: How Stories Go Viral and Drive Major Economic Events, Princeton University Press. Available online: https:\/\/press.princeton.edu\/books\/hardcover\/9780691182292\/narrative-economics.","DOI":"10.2307\/j.ctvdf0jm5"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mou, Y., Zhou, L., Chen, W., Liu, J., and Li, T. (2025). Filter learning-based partial least squares regression and its application in infrared spectral analysis. Algorithms, 18.","DOI":"10.3390\/a18070424"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Emura, T., Matsumoto, K., Uozumi, R., and Michimae, H.G. (2024). Ridge: An R package for generalized ridge regression for sparse and high-dimensional linear models. Symmetry, 16.","DOI":"10.20944\/preprints202401.1119.v1"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2017.07.007","article-title":"Random walks and diffusion on networks","volume":"716\u2013717","author":"Masuda","year":"2017","journal-title":"Phys. Rep."},{"key":"ref_50","unstructured":"Lambiotte, R., Delvenne, J.-C., and Barahona, M. (2008). Laplacian dynamics and multiscale modular structure in networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/0812.1770."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), Doha, Qatar. Available online: https:\/\/aclanthology.org\/D14-1162.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_52","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2025, November 10). Distributed Representations of Words and Phrases and Their Compositionality. Advances in Neural Information Processing Systems 26 (NeurIPS 2013), 2013; pp. 3111\u20133119. Available online: https:\/\/papers.nips.cc\/paper\/2013\/hash\/9aa42b31882ec039965f3c4923ce901b-Abstract.html."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:47:16Z","timestamp":1767192436000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,31]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["info17010022"],"URL":"https:\/\/doi.org\/10.3390\/info17010022","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,31]]}}}