Moscow, Russian Federation
Orel, Russian Federation
High competition requires new methods, tools, and technologies for implementing marketing efforts. Mind maps represent marketing information in a compact way, which facilitates classification and visualization of the marketing phenomenon under analysis. The method of text analysis can be applied to marketing research because mind maps that represent marketing information are a structured text; such an approach makes it possible to subject mind maps to linguistic processing. This research combined the conventional semantic analysis with comparing mind maps as a method of semantic-graph analysis. Initial marketing data were converted into a structured graph of lexical units, which were them compared pairwise to calculate similarity, taking into account the structural-multiple approach. The methods of semantic text analysis proved applicable to marketing data and effective in extracting additional information from a set of heterogeneous sources or different experts. Text analysis tools demonstrated a good potential for broadening the rage of marketing research methods.
marketing, market, analysis, mind maps, linguistics, strategy, semantic analysis, information extraction
1. Gaus A. S. Consumer portrait research as a direction of marketing research. Tribuna uchenogo, 2022, (10): 102–107. (In Russ.) https://elibrary.ru/encqea
2. Madieva Z. Organizing marketing research and information on Internet marketing. Bulletin of Science and Practice, 2021, 7(4): 332–338. https://doi.org/10.33619/2414-2948/65/38
3. Kozyrev P. E. On the question of methods and tools information support of marketing research using Internet technologies. Vestnik Gzhelskogo gosudarstvennogo universiteta, 2022, (6): 183–192. (In Russ.) https://elibrary.ru/jsfxbr
4. Iskoskov M. O., Kargina E. V. Market research management by the multi-dimensional cluster analysis. Vestnik Volzhskogo universiteta im. V. N. Tatishcheva, 2019, 2(3): 68–73. (In Russ.) https://elibrary.ru/hggrrk
5. Ivanova V. A., Saenko I. I. Application of BIG DATA analysis in marketing research. Alleia nauki, 2018, 6(5): 1120–1123. (In Russ.) https://elibrary.ru/utnztg
6. Lapshova O. A., Lyalkova I. O. Neuromarketing and people meter as methods of marketing research. Sotsialno-psikhologicheskie problemy mentalnosti / mentaliteta, 2020, (16): 109–114. (In Russ.) https://elibrary.ru/tymykq
7. Esaulenko A. V., Rudskaya E. N. Innovative methods in marketing research. Sovremennye nauchnye issledovaniia i razrabotki, 2017, (8): 190–196. (In Russ.) https://elibrary.ru/ymjdvl
8. Sharoiko F. V., Grunina A. A. Improving the effectiveness of marketing research using the qualitative and quantitative method of semantic differential. Delta nauki, 2018, (2): 58–63. (In Russ.) https://elibrary.ru/yunrrj
9. Sknarev D. S. Reclamemics and linguistic marketing as new knowledge areas. Vestnik Rossiiskogo universiteta druzhby narodov. Seriia: Russkii i inostrannye iazyki i metodika ikh prepodavaniia, 2016, (3): 51–57. https://elibrary.ru/wlsgit
10. Ratnapuri C. I., Aprilia S., Ningrum D. K., Sudirman I. D., Alamsyah D. P. The mindmapping for marketing strategy: Case study of fashion industry. IOP Conference Series: Earth and Environmental Science: Proc. 4th Intern. Conf. on Eco Engineering Development, Banten, 10–11 Nov 2020. Banten: IOP Publishing Ltd, 2021, vol. 794. https://doi.org/10.1088/1755-1315/794/1/012082
11. Kudryavtsev D., Gavrilova T. From anarchy to system: A novel classification of visual knowledge codification techniques. Knowledge and Process Management, 2017, 24(1): 3–13. https://doi.org/10.1002/kpm.1509
12. Bhattacharya D., Mohalik R. Digital mind mapping software: A new horizon in the modern teaching-learning strategy. Journal of Advances In Education and Philosophy, 2020, 4(10): 400–406. http://dx.doi.org/10.36348/jaep.2020.v04i10.001
13. Feshchenko L. G. Pentagram of advertising text or the complex analysis technique (pre-text, con-text, text, sub-text, after-text). Genres and types of text in scientific and media discourse: Proc. Conf., Orel, 18–19 Sep 2020. Orel: OSIC, 2020, iss. 17, 21–35. (In Russ.) https://elibrary.ru/lsbewq
14. Ehlakov Yu. P., Malakhovskaya E. K. Semantic network for forming the content of text messages for the promotion of mobile applications to the consumer market. Vestnik Dagestanskogo gosudarstvennogo tehnicheskogo universiteta. Tehnicheskie nauki, 2018, 1(45): 129–138. (In Russ.) https://elibrary.ru/xptylz
15. Alexeeva T. E., Fedoseeva L. N. English advertising slogans of automakers: Structural-semantic analysis. Proceedings of VSU. Series: Linguistics and intercultural communication, 2022, (1): 81–87. (In Russ.) https://doi.org/10.17308/lic.2022.1/9002
16. Shlykov V. A., Gordeeva O. A. Formation of contextual advertising based on the analysis of the users network activity. Trudy mezhdunarodnogo simpoziuma "Nadezhnost i kachestvo", 2020, 1: 276–280. (In Russ.) https://elibrary.ru/bzwhse
17. Shimokhin A. V. Semantic analysis of supplier reviews based on the use of neural network technology. Fundamental research, 2021, (5): 117–121. (In Russ.) https://doi.org/10.17513/fr.43048
18. Starov S. A., Gladkikh I. V., Muravskii D. V. Research of brand associations for building strategic brand maps. Marketing i marketingovye issledovaniia, 2019, (2): 116–130. (In Russ.) https://elibrary.ru/piopuf
19. Grech G. Marketing mind maps in higher education. Symposia Melitensia, 2016, 12: 107–116.
20. Beel J., Langer S. An exploratory analysis of mind maps. DocEng'11: Proc. 11th ACM Symposium on Document Engineering, Mountain View, 19–22 Sep 2011. NY: ACM, 2011, 81–84. http://dx.doi.org/10.1145/2034691.2034709
21. Kedaj P., Pavlicek J., Hanzlik P. Effective mind maps in e-learning. Acta Informatica Pragensia, 2014, 3(3): 239–250. https://doi.org/10.18267/j.aip.51
22. Chen T.-Ju, Mohanty R. R., Hoffmann Rodriguez M. A., Krishnamurthy V. R. Collaborative mind-mapping: A study of patterns, strategies, and evolution of maps created by peer-pairs. ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: Proc. 31st Intern. Conf. on Design Theory and Methodology, Anaheim, 18–21 Aug 2019. Anaheim: ASME, 2019. http://dx.doi.org/10.1115/DETC2019-98125
23. Koznov D., Larchik E., Pliskin M., Artamonov N. Mind maps merging in collaborative work. Programming and Computer Software, 2011, 37, 315–321. https://doi.org/10.1134/S036176881106003X
24. Lindholm T. A 3-way merging algorithm for synchronizing ordered trees – the "3DM" merging and differencing tool for XML. Helsinki: Helsinki University of Technology, 2001, 128.
25. Jamieson P., Eaton J. Towards a better graphlet-based mind map metric for automating student feedback. Innovative Use of Technology II: Proc. 122nd ASEE Annual Conf. & Exposition, Seattle, 14–17 Jun 2015. Seattle: American Society for Engineering Education, 2015. https://doi.org/10.18260/p.24924
26. Zhang Z., Gentile A., Ciravegna F. Recent advances in methods of lexical semantic relatedness – a survey. Natural Language Engineering, 2013, 19(4): 411–479. http://doi.org/10.1017/S1351324912000125
27. Bermudez S. J. G. Method for measuring the semantic-similarity of textual documents. Izvestiya SFedU. Engineering Sciences, 2017, (3): 17–29. (In Russ.) https://elibrary.ru/zdhxjr
28. Shalaby W., Zadrozny W. Mined semantic analysis: A new concept space model for semantic representation of textual data. 2017 IEEE: Proc. Intern. Conf. on Big Data (Big Data), Boston, 11–14 Dec 2017. Boston: IEEE, 2017, 2122–2131. https://doi.org/10.1109/BigData.2017.8258160
29. Soleimandarabi M. N., Mirroshandel S. A., Sadr H. A survey of semantic relatedness measures. International journal of Computer Science & Network Solutions, 2015, 3(2). http://dx.doi.org/10.13140/RG.2.2.17358.69449
30. Baroni M., Dinu G., Kruszewski G. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. ACL 2014: Proc. 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, 23–25 Jun 2014. Kerrville: ACL, 2014, vol. 1: 238–247. https://doi.org/10.3115/v1/P14-1023
31. Landauer T. K., Laham D., Rehder B., Schreiner M. E. How well can passage meaning be derived without using word order? A comparison of latent semantic analysis and humans. Proceedings of the 19th Annual Meeting of the Cognitive Science Society. Boulder: University of Colorado Boulder, 1997, 412–417.
32. Blei D. M., Ng A. Y., Jordan M. I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022.
33. Collobert R., Weston J. A Unified architecture for natural language processing: Deep neural networks with multitask learning. ICML'08: Proc. 25th Intern. Conf. on Machine Learning. NY: ACM, 2008, 160–167. https://doi.org/10.1145/1390156.1390177
34. Mikolov T., Chen K., Corrado G., Dean J. Efficient estimation of word representations in vector space. Proceeding of Workshop at International Conference on Learning Representations, 2013. https://doi.org/10.48550/arXiv.1301.3781
35. Pennington J., Socher R., Manning C. GloVe: Global vectors for word representation. EMNLP 2014: Proc. of the 2014 Conf. on Empirical Methods in Natural Language Processing, Doha, 25–29 Oct 2014. Kerrville: ACL, 1532–1543. https://doi.org/10.3115/v1/D14-1162
36. WordNet: An electronic lexical database, ed. Fellbaum Ch. Cambridge: MIT Press, 1998, 422. https://doi.org/10.7551/mitpress/7287.001.0001
37. Gabrilovich E., Markovitch S. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. IJCAI-07: Proc. Twentieth Intern. Joint Conference on Artificial Intelligence, Hyderabad, 6–12 Jan 2007. Menlo Park: AAAI Press, 2007, vol. 6: 1606–1611.
38. Hassan S., Mihalcea R. Semantic relatedness using salient semantic analysis. AAAI Technical Track: Natural Language Processing: Proc. Twenty-Fifth AAAI Conf. on Artificial Intelligence, San Francisco, 7 – 11 Aug 2011. Palo Alto: AAAI Press, 2011, 25(1): 884–889. https://doi.org/10.1609/aaai.v25i1.7971
39. Camacho-Collados J., Pilehva M. T., Navigli R. NASARI: A novel approach to a semantically-aware representation of items. NAACL-HLT 2015: Proc. 2015 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, 31 May–5 Jun 2015. Kerrville: ACL, 2015, 567–577. http://dx.doi.org/10.3115/v1/N15-1059
40. Li P., Wang H., Zhu K. Q., Wang Z., Wu X. Computing term similarity by large probabilistic is a knowledge. CIKM’13: Proc. 22nd ACM Intern. Conf. on Information & Knowledge Management, San Francisco, 27 Oct–1 Nov 2013. NY: ACM, 2013, 1401–1410. https://doi.org/10.1145/2505515.2505567
41. Kim D., Wang H., Oh A. Context-dependent conceptualization. IJCAI’13: Proc. Twenty-Third Intern. Joint Conf. on Artificial Intelligence, Beijing, 3–9 Aug 2013. Menlo Park: AAAI Press, 2013, 2654–2661.
42. Song Y., Roth D. On dataless hierarchical text classification. Main Track: NLP and Machine Learning: Proc. Twenty-Eighth AAAI Conf. on Artificial Intelligence, Québec, 27–31 Jul 2014. Palo Alto: AAAI Press, 2014, 28(1): 1579–1585. https://doi.org/10.1609/aaai.v28i1.8938
43. Egozi O., Markovitch S, Gabrilovich E. Concept-based information retrieval using explicit semantic analysis. ACM Transactions on Information Systems, 2011, 29(2). https://doi.org/10.1145/1961209.1961211
44. Wang Z., Zhao K., Wang H., Meng X., Wen Ji-R. Query understanding through knowledge-based conceptualization. IJCAI’15: Proc. 24th Intern. Conf. on Artificial Intelligence, Buenos Aires, 25–31 Jul 2015. Palo Alto: AAAI Press, 2015, 3264–3270.
45. Hua W., Wang Z., Wang H., Zheng K., Zhou X. Short text understanding through lexical-semantic analysis. ICDE 2015: 31st Intern. Conf. on Data Engineering, Seoul, 13–17 Apr 2015. NY: IEEE, 2015, 495–506. https://doi.org/10.1109/ICDE.2015.7113309
46. Deerwester S. C., Dumais S. T., Landauer T. K. , Furnas G. W., Harshman R. A. Indexing by Latent Semantic Analysis. JASIST, 1990, 41(6): 391–407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
47. Hughes T., Ramage D. Lexical semantic relatedness with random graph walks. EMNLP-CoNLL 2007: Proc. 2007 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, 28–30 Jun 2007. Kerrville: ACL, 2007, 581–589.
48. Yang D., Yin Y. Evaluation of taxonomic and neural embedding methods for calculating semantic similarity. Natural Language Engineering, 2022, 28(6): 733–761. https://doi.org/10.1017/S1351324921000279
49. Kokhov V. A., Ibrahim A. R., Kokhov V. V. System of models for the analysis of graph's similarity with account of circuit arrangement. Vestnik Moskovskogo energeticheskogo instituta, 2009, (5): 5–13. (In Russ.) https://elibrary.ru/kxnbur
50. Pogrebnoi A. V. A method for determining the similarity of graph structures based on the allocation of partial isomorphism in geoinformatics problems. Izvestiia Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov, 2015, 326(11): 56–66. (In Russ.) https://elibrary.ru/vqwbxv
51. Labriji A., Charkaoui S., Abdelbaki I., Namir A., Labriji E. H. Similarity measure of graphs. International Journal of Recent Contributions from Engineering, Science & IT, 2017, 5(2): 42–56. https://doi.org/10.3991/ijes.v5i2.7251
52. Ma G., Ahmed N. K., Willke T. L., Yu Ph. S. Deep graph similarity learning: A survey. Data Mining and Knowledge Discovery, 2021, 35, 688–725. https://doi.org/10.1007/s10618-020-00733-5
