Implementation of Text Mining in Socio-Economic Research

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Author(s): Konstantin Malyshenko (Humanitarian and Pedagogical Academy, Crimean Federal University Named After V.I. Vernadsky, Russia), Vadim Malyshenko (Humanitarian and Pedagogical Academy, Crimean Federal University Named After V.I. Vernadsky, Russia), Marina Anashkina (Humanitarian and Pedagogical Academy, Crimean Federal University Named After V.I. Vernadsky, Russia)and Dmitry Anashkin (Crimean Federal University Named After V.I. Vernadsky, Russia)
Copyright: 2024
Volume: 19
Issue: 1
Pages: 21
Source title: International Journal of Business Data Communications and Networking (IJBDCN)
DOI: 10.4018/IJBDCN.341263

Keywords: Information Science Reference / Media & Communications / Networking and Telecommunications / Telecommunications

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Abstract

This work aims to analyze insights from social networks for identification of population satisfaction with pay level in Russia using the text mining approach. For this, a sentiment analysis framework was developed, which integrates Twitter mining tools and a sentiment index. Sentiments were extracted using Twitter mining and then recoded and substituted into the sentiment formula. The results of sentiment analysis indicate low satisfaction with levels of pay among Russians. Twitter was chosen as the object of research, as one of the most active and independent networks in Russia. It is possible that some of the tweets belong to authors who are not living in Russia at the moment, but their number is not significant and their interest in this issue, in the authors' opinion, only enhances the relevance of the problem under study.

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Implementation of Text Mining in Socio-Economic Research

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