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Approach for Multi-Label Text Data Class Verification and Adjustment Based on Self-Organizing Map and Latent Semantic Analysis
Volume 33, Issue 1 (2022), pp. 109–130
Pub. online: 10 January 2022      Type: Research Article      Open accessOpen Access
Received
1 June 2021
Accepted
1 January 2022
Published
10 January 2022

Abstract

In this paper, a new approach has been proposed for multi-label text data class verification and adjustment. The approach helps to make semi-automated revisions of class assignments to improve the quality of the data. The data quality significantly influences the accuracy of the created models, for example, in classification tasks. It can also be useful for other data analysis tasks. The proposed approach is based on the combination of the usage of the text similarity measure and two methods: latent semantic analysis and self-organizing map. First, the text data must be pre-processed by selecting various filters to clean the data from unnecessary and irrelevant information. Latent semantic analysis has been selected to reduce the vectors dimensionality of the obtained vectors that correspond to each text from the analysed data. The cosine similarity distance has been used to determine which of the multi-label text data class should be changed or adjusted. The self-organizing map has been selected as the key method to detect similarity between text data and make decisions for a new class assignment. The experimental investigation has been performed using the newly collected multi-label text data. Financial news data in the Lithuanian language have been collected from four public websites and classified by experts into ten classes manually. Various parameters of the methods have been analysed, and the influence on the final results has been estimated. The final results are validated by experts. The research proved that the proposed approach could be helpful to verify and adjust multi-label text data classes. 82% of the correct assignments are obtained when the data dimensionality is reduced to 40 using the latent semantic analysis, and the self-organizing map size is reduced from 40 to 5 by step 5.

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Biographies

Stefanovič Pavel
pavel.stefanovic@vilniustech.lt

P. Stefanovič received a PhD degree in computer science from the Institute of Mathematics and Informatics, Vilnius University, Lithuania, in 2015. He is currently employed as a researcher and associate professor at the Faculty of Fundamental Sciences, Vilnius Gediminas Technical University. His research interests include data mining methods, natural language pre-processing, machine learning methods, visualization of multidimensional data, data clustering methods. He is the author of 12 publications.

Kurasova Olga
olga.kurasova@mif.vu.lt

O. Kurasova received a PhD degree in computer science from the Institute of Mathematics and Informatics, Vytautas Magnus University, Lithuania, in 2005. She is currently employed as a principal researcher and a professor at the Institute of Data Science and Digital Technologies, Vilnius University. Her research interests include data mining methods, optimization theory and applications, artificial intelligence, neural networks, visualization of multidimensional data, multiple criteria decision support, parallel computing, and image processing. She is the author of more than 80 scientific publications.


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Keywords
multi-label text data clustering self-organizing map latent semantic analysis Lithuanian language

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