The algorithm of modelling and analysis of latent semantic relations: Linear algebra vs. probabilistic topic models

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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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Rizun, N., Taranenko, Y., & Waloszek, W. (2017). The algorithm of modelling and analysis of latent semantic relations: Linear algebra vs. probabilistic topic models. In Communications in Computer and Information Science (Vol. 786, pp. 53–68). Springer Verlag. https://doi.org/10.1007/978-3-319-69548-8_5

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