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Scientific and Technical Information Processing

Erschienen in:

01.12.2023

Trend Detection Using NLP as a Mechanism of Decision Support

verfasst von: P. A. Lobanova, I. F. Kuzminov, E. Yu. Karatetskaia, E. A. Sabidaeva, V. V. Anpilogov

Erschienen in: Scientific and Technical Information Processing | Ausgabe 5/2023

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Abstract

The purpose of this article is to present the principles of a developed algorithm for identifying trends based on the analysis of big text data and presenting the result in formats that are convenient for decision makers to be implemented in the iFORA Big Data Mining System. The paper provides an overview of existing text analytics algorithms; outlines the mathematical basis for identifying terms that mean trends, which is proposed and tested for dozens of implemented projects; describes approaches to clustering terms based on their vectors in the Word2vec space; and provides examples of two key visualizations (semantic, trend maps) that outline the range of topics and trends that characterize a particular area of study, as a way to adapt the results of the analysis to the tasks of decision makers. The limitations and advantages of using the proposed approach for decision support are discussed, and directions for future research are suggested.

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https://www.elastic.co/guide/en/elasticsearch/reference/master/search-aggregations-bucket-significantterms-aggregation. html#_jlh_score

Pappa, G.L. and Freitas, A., Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach, Natural Computing Series, Berlin: Springer, 2010. https://doi.org/10.1007/978-3-642-02541-9_5CrossRef

Yuan, Ye., Sun, P., and Fan, H., Automatic selection and evaluation on data mining algorithms, 2015 6th IEEE Int. Conf. on Software Engineering and Service Science (ICSESS), Beijing, 2015, IEEE, 2015, pp. 29–32. https://doi.org/10.1109/icsess.2015.7339000

Porter, A.L. and Zhang, Y., Tech mining of science & technology information resources for future-oriented technology analyses, Futures Res. Methodology Version, 2015, vol. 3.

Zhu, D. and Porter, A.L., Automated extraction and visualization of information for technological intelligence and forecasting, Technol. Forecast. Soc. Change, 2002, vol. 69, no. 5, pp. 495–506. https://doi.org/10.1016/s0040-1625(01)00157-3CrossRef

Osipov, G., Smirnov, I., Tikhomirov, I., Sochenkov, I., Shelmanov, A., and Shvets, A., Information retrieval for R&D support, Professional Search in the Modern World, Paltoglou, G., Loizides, F., and Hansen, P., Eds., Lecture Notes in Computer Science, vol. 8830, Cham: Springer, 2014, pp. 45–69. https://doi.org/10.1007/978-3-319-12511-4_4CrossRef

Newman, N.C., Porter, A.L., Newman, D., Trumbach, Ch.C., and Bolan, S.D., Comparing methods to extract technical content for technological intelligence, J. Eng. Technol. Manage., 2014, vol. 32, pp. 97–109. https://doi.org/10.1016/j.jengtecman.2013.09.001CrossRef

Tseng, Yu., Lin, C., and Lin, Y.I., Text mining techniques for patent analysis, Inf. Process. Manage., 2007, vol. 43, no. 5, pp. 1216–1247. https://doi.org/10.1016/j.ipm.2006.11.011CrossRef

Cooke, P., Gomez Uranga, M.G., and Etxebarria, G., Regional innovation systems: Institutional and organisational dimensions, Res. Policy, 1997, vol. 26, nos. 4–5, pp. 475–491. https://doi.org/10.1016/s0048-7333(97)00025-5CrossRef

Kwakkel, J.H., Carley, S., Chase, J., and Cunningham, S.W., Visualizing geo-spatial data in science, technology and innovation, Technol. Forecast. Soc. Change, 2014, vol. 81, pp. 67–81. https://doi.org/10.1016/j.techfore.2012.09.007CrossRef

10.

Feldman, R., Fresko, M., Kinar, Ya., Lindell, Ye., Liphstat, O., Rajman, M., Schler, Yo., and Zamir, O., Text mining at the term level, Principles of Data Mining and Knowledge Discovery, Żytkow, J.M. and Quafafou, M., Eds., Lecture Notes in Computer Science, vol. 1510, Berlin: Springer, 1998, pp. 65–73. https://doi.org/10.1007/bfb0094806CrossRef

11.

Averbuch, M., Context-sensitive medical information retrieval, MEDINFO 2004, Fieschi, M., Coiera, E., and Li, Yu-Ch.J., Eds., Studies in Health Technology and Informatics, vol. 107, IOS Press, 2004, pp. 282–286. https://doi.org/10.3233/978-1-60750-949-3-282

12.

Osipov, G., Smirnov, I., Tikhomirov, I., Sochenkov, I., and Shelmanov, A., Exactus expert—search and analytical engine for research and development support, Novel Applications of Intelligent Systems, Hadjiski, M., Kasabov, N., Filev, D., and Jotsov, V., Eds., Studies in Computational Intelligence, vol. 586, Cham: Springer, 2016, pp. 269–285. https://doi.org/10.1007/978-3-319-14194-7_14

13.

Church, K.W., A stochastic parts program and noun phrase parser for unrestricted text, Int. Conf. on Acoustics, Speech, and Signal Processing, Glasgow, 1988, IEEE, 1988, pp. 695–698. https://doi.org/10.1109/icassp.1989.266522

14.

Wang, B., Liu, S., Ding, K., Liu, Z., and Xu, J., Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology, Scientometrics, 2014, vol. 101, no. 1, pp. 685–704. https://doi.org/10.1007/s11192-014-1342-3CrossRef

15.

Frantzi, K., Ananiadou, S., and Mima, H., Automatic recognition of multi-word terms: The C-value/NC-value method, Int. J. Digital Libr., 2000, vol. 3, no. 2, pp. 115–130. https://doi.org/10.1007/s007999900023CrossRef

16.

Javed, Z. and Afzal, H., Biomedical text mining for concept identification from traditional medicine literature, 2014 Int. Conf. on Open Source Systems & Technologies, Lahore, Pakistan, 2014, IEEE, 2014, pp. 206–211. https://doi.org/10.1109/icosst.2014.7029345

17.

Rose, S., Engel, D., Cramer, N., and Cowley, W., Automatic keyword extraction from individual documents, Text Mining: Applciations and Theory, Berry, M.W. and Kogan, J., Eds., John Wiley & Sons, 2010, pp. 1–20. https://doi.org/10.1002/9780470689646.ch1CrossRef

18.

Salton, G. and Yu, C.T., On the construction of effective vocabularies for information retrieval, ACM SIGPLAN Not., 1973, vol. 10, no. 1, pp. 48–60. https://doi.org/10.1145/951787.951766CrossRef

19.

Liu, C., Sheng, Ya., Wei, Z., and Yang, Yo., Research of text classification based on improved TF-IDF algorithm, 2018 IEEE Int. Conf. of Intelligent Robotic and Control Engineering (IRCE), Lanzhou, China, 2018, IEEE, 2018, pp. 218–222. https://doi.org/10.1109/irce.2018.8492945

20.

Kutuzov, A., Kuzmenko, E., and Pivovarova, L., Clustering of Russian adjective-noun constructions using word embeddings, Proc. 6th Workshop on Balto-Slavic Natural Language Processing, Valencia: Association for Computational Linguistics, 2017, pp. 3–13. https://doi.org/10.18653/v1/w17-1402

21.

Kumar, G. and Kumar, K., An information theoretic approach for feature selection, Secur. Commun. Networks, 2013, vol. 5, no. 2, pp. 178–185. https://doi.org/10.1002/sec.303CrossRef

22.

Turney, P.D., Mining the Web for synonyms: PMI-IR versus LSA on TOEFL, Machine Learning: ECML 2001, Da Raedt, L. and Flach, P., Eds., Lecture Notes in Computer Science, vol. 2167, Berlin: Springer, 2001, pp. 491–502. https://doi.org/10.1007/3-540-44795-4_42

23.

Ahmad, K. and Davies, A.E., Weirdness in special-language text: Welsh radioactive chemicals texts as an exemplar, Int. Inst. Terminologieforschung J., 1994, vol. 5, no. 2, pp. 22–52.

24.

Steinhaus, H., Sur la division des corps materiels en parties, Bull. Acad. Polon. Sci., C, 1956, vol. 4, pp. 801–804.

25.

Han, J., Kamber, M., and Pei, J., Classification, The Morgan Kaufmann Series in Data Management, San Francisco: Morgan Kaufmann, 2001. https://doi.org/10.1016/C2009-0-61819-5CrossRef

26.

Bae, S. and Yi, Yo., Acceleration of word2vec using GPUs, Neural Information Processing. ICONIP 2016, Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., and Liu, D., Eds., Lecture Notes in Computer Science, vol. 9948, Cham: Springer, 2016, pp. 269–279. https://doi.org/10.1007/978-3-319-46672-9_31CrossRef

27.

Waskom, M.L., Seaborn: Statistical data visualization, J. Open Source Software, 2021, vol. 6, no. 60, p. 3021. https://doi.org/10.21105/joss.03021ADSCrossRef

Titel: Trend Detection Using NLP as a Mechanism of Decision Support
verfasst von: P. A. Lobanova
I. F. Kuzminov
E. Yu. Karatetskaia
E. A. Sabidaeva
V. V. Anpilogov
Publikationsdatum: 01.12.2023
Verlag: Pleiades Publishing
Erschienen in: Scientific and Technical Information Processing / Ausgabe 5/2023
Print ISSN: 0147-6882
Elektronische ISSN: 1934-8118
DOI: https://doi.org/10.3103/S0147688223050106

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