Public Resources for Detecting Mentions of Medical Data in Russian Social Media

Lobantsev, Artem; Loginova, Victoria; Burlakova, Yulia; Andreev, N. E.; Matveeva, Victoria I.; Филимонова, И.В.; Dobrenko, Natalia; Gusarova, Natalia

doi:10.1007/978-3-030-65218-0_35

Cited by 1 publication

(1 citation statement)

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“…The paper notes that text preprocessing (summation and lemmatization) is a mandatory step; ignoring it will decrease the accuracy of the results. Based on data from Russian social networks, Lobantsev et al (2020) investigate the effectiveness of passive and active data collection methods for further analysis. In the article "Extraction of structural elements of inventions from Russian-language patents," Korobkin et al (2019) explore the problem of extracting structured data from Russian-language patents in synthesizing new technical solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Text Mining in Socio-Economic Research

Malyshenko,

Anashkina

et al. 2024

International Journal of Business Data Communications and Networking

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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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Section: Literature Reviewmentioning

confidence: 99%