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A study of the applicability of LSTM recurrent networks in the task of searching for social network experts
Banokin Pavel Ivanovich

Assistant, Tomsk Polytechnic University

634028, Russia, Tomskaya oblast', g. Tomsk, ul. Lenina, 2, of. 103a

Efremov Aleksandr Aleksandrovich

Assistant, Tomsk Polytechnic University

634028, Russia, Tomskaya oblast', g. Tomsk, ul. Lenina, 2, of. 115a

Luneva Elena Evgenevna

PhD in Technical Science

Associate Professor, Tomsk Polytechnic University

634028, Russia, Tomskaya oblast', g. Tomsk, ul. Lenina, 2, of. 115a

Kochegurova Elena Alekseevna

PhD in Technical Science

Associate Professor, Tomsk Polytechnic University

634028, Russia, Tomskaya oblast', g. Tomsk, ul. Lenina, 2, of. 112a



The article explores the applicability of long short-term memory (LSTM) recurrent networks for the binary classification of text messages of the social network Twitter. A three-stage classification process has been designed, allowing a separate analysis of pictograms and verification of the text for neutrality. The accuracy of the classification of the emotional polarity of text messages using the LSTM network and vector representations of words was verified. The percentage of coincidences of vector representations of words with a training set of data is determined, which makes it possible to obtain an acceptable classification accuracy. The estimation of the learning speed of the LSTM network and the use of memory was carried out. To solve the task of classifying text messages, methods of processing natural language and machine learning using precedents are applied. The algorithmic base for processing text data from social networks, obtained as a result of the application of LSTM neural networks, has been optimized. The novelty of the proposed solution method is due to the implementation of pre-processing of messages, which allows to improve the accuracy of classification, and the use of the neural network configuration taking into account the specifics of text data of social networks.

Keywords: Twitter, word embeddings, social networks, LSTM networks, sentiment analysis, natural language processing, recurrent neural networks, text data preprocessing, reccurent network, binary classification



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