Abstract
This paper presents the algorithm of modelling and analysis of Latent Semantic Relations inside the argumentative type of documents collection. The novelty of the algorithm consists in using a systematic approach: in the combination of the probabilistic Latent Dirichlet Allocation (LDA) and Linear Algebra based Latent Semantic Analysis (LSA) methods; in considering each document as a complex of topics, defined on the basis of separate analysis of the particular paragraphs. The algorithm contains the following stages: modelling and analysis of Latent Semantic Relations consistently on LDA- and LSA-based levels; rules-based adjustment of the results of the two levels of analysis. The verification of the proposed algorithm for subjectively positive and negative Polish-language film reviews corpuses was conducted. The level of the recall rate and precision indicator, as a result of case study, allowed to draw the conclusions about the effectiveness of the proposed algorithm.
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References
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Wokingham (2011). Second edition (1999)
Bahl, L., Baker, J., Jelinek, E., Mercer, R.: Perplexity – a measure of the difficulty of speech recognition tasks. In: Program, 94th Meeting of the Acoustical Society of America, vol. 62, p. S63 (1977)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Blei, D.: Introduction to probabilistic topic models. Comm. ACM 55(4), 77–84 (2012)
Ali, D., Juanzi, L., Lizhu, Z., Faqir, M.: Knowledge discovery through directed probabilistic topic models: a survey. In: Proceedings of Frontiers of Computer Science in China, pp. 280–301 (2010)
Blei, D.: Topic modeling, http://www.cs.princeton.edu/~blei/topicmodeling.html
Dumais, S.T., Furnas, G.W., Landauer, T.K., Deerwester, S.: Using latent semantic analysis to improve information retrieval. In: Proceedings of CHI 1988: Conference on Human Factors in Computing, pp. 281–285. ACM, New York (1988)
Deerwester, S., Dumais, S.T., Harshman, R.: Indexing by Latent Semantic Analysis (1990), http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf
Eden, L.: Matrix Methods in Data Mining and Pattern Recognition. SIAM (2007)
Furnas, G.W., Deerwester, S., Dumais, S.T., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular value decomposition model of latent semantic structure. In: Proceedings of ACM SIGIR Conference, pp. s.465–s.480. ACM, New York (1998)
Salton, G., Michael, J.: McGill Introduction to Modern Information Retrieval. McGraw-Hill Computer Science Series, vol. XV, 448 p. McGraw-Hill, New York (1983)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3) (1999)
Gramacki, J., Gramacki, A.: Metody algebraiczne w zadaniach eksploracji danych na przykładzie automatycznego analizowania treści dokumentów. In: XVI Konferencja PLOUG, pp. 227–249 (2010)
Kapłanski, P., Rizun, N., Taranenko, Y., Seganti, A.: Text-mining similarity approximation operators for opinion mining in bi tools. In: Proceeding of the 11th Scientific Conference “Internet in the Information Society-2016”, pp. 121–141. University of Dąbrowa Górnicza (2016)
Canini, K.R., Shi, L., Griffiths, T.: Online inference of topics with latent dirichlet allocation. J. Mach. Learn. Res. Proc. Track 5, 65–72 (2009)
Tomanek, K.: Analiza sentymentu – metoda analizy danych jakościowych. Przykład zastosowania oraz ewaluacja słownika RID i metody klasyfikacji Bayesa w analizie danych jakościowych, Przegląd Socjologii Jakościowej, pp. 118–136 (2014), www.przegladsocjologiijakosciowej.org
Aggarwal, C., Zhai, X.: Mining Text Data. Springer, New York (2012)
Leticia, H.A.: Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers, Doctor of Philosophy (Management Science), 226 p. (2011)
Papadimitrious, C.H., Raghavan, P., Tamaki, H., Vempala, S.: Latent semantic indexing: a probabilistic analysis. J. Comput. Syst. Sci. 61, 217–235 (2000)
Rizun, N., Kapłanski, P., Taranenko, Y.: Development and research of the text messages semantic clustering methodology. In: 2016, Third European Network Intelligence Conference, vol. # 33, pp. 180–187. ENIC (2016)
Rizun, N., Kapłanski, P., Taranenko, Y.: Method of a Two-Level Text-Meaning Similarity Approximation of the Customers’ Opinions. Economic Studies – Scientific Papers. University of Economics in Katowice, vol. 296, pp. 64–85 (2016)
Rizun, N., Taranenko, Y.: Development of the algorithm of polish language film reviews preprocessing. In: Proceeding of the 2nd International Conference on Information Technologies in Management, Rocznik Naukowy Wydziału Zarządzania WSM (in print) (2017)
Rui, X., Donald, C., Wunsch, I.I.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), s.613–s.620 (1975)
Hofman, T.: Probabilistic latent semantic analysis. In: UAI 1999, pp. 289–296 (1999); Hofmann, T.: Probabilistic Latent Semantic Indexing. In: SIGIR, pp. 50–57 (1999)
Mika, T.: Term Weighting in Short Documents for Document Categorization, Keyword Extraction and Query Expansion. PhD Thesis, Series of Publications A, Report A-2013-1 (2013)
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Rizun, N., Taranenko, Y., Waloszek, W. (2017). The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_5
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