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Information 2018, 9(12), 307; https://doi.org/10.3390/info9120307

Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations

1
Department of Applied Informatics in Management, Faculty of Management and Economics, Gdansk University of Technology, 80-233 Gdańsk, Poland
2
Department of Computer Integrated Techniques and Metrology, Ukrainian State Chemical Technology University, 49000 Dnipro, Ukraine
3
Department of Software Engineering, Faculty of Electronics, Telecommunications and Informatics Gdansk University of Technology, 80-233 Gdańsk, Poland
This manuscript is an extended version of our paper: The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models. In Proceedings of the 8th International Conference on Knowledge Engineering and Semantic Web, Szczecin, Poland, 8–10 November 2017; pp. 53–68.
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Author to whom correspondence should be addressed.
Received: 15 October 2018 / Revised: 19 November 2018 / Accepted: 28 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Abstract

The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved by providing the possibility of the joint usage of the following methods: (1) retrieval and recognition of the hierarchical semantic structure of the text and (2) development of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP. The main scientific contribution of this research is the set of the following approaches: at the phase of LSR revealing (1) combination of the discriminant and probabilistic models while applying the rules of adjustments to obtain the final joint result; at all SCP phases (2) considering document as a complex structure of topically completed textual components (paragraphs) and (3) taking into account the features of persuasive documents’ type. The experimental results have demonstrated the enhancement of the SCP accuracy, namely significant increase of average values of recall and precision indicators and guarantee of sufficient accuracy level. View Full-Text
Keywords: sentiment classification; topic modelling; Latent Semantic Analysis; Latent Dirichlet Allocation; hierarchical sentiment dictionary; contextually-oriented hierarchical corpus; text tonality; accuracy sentiment classification; topic modelling; Latent Semantic Analysis; Latent Dirichlet Allocation; hierarchical sentiment dictionary; contextually-oriented hierarchical corpus; text tonality; accuracy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rizun, N.; Taranenko, Y.; Waloszek, W. Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations. Information 2018, 9, 307.

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