EFFECTIVENESS STUDY OF VARIOUS ARCHITECTURES OF RECURRENT NEURAL NETWORKS FOR ANALYZING THE SENTIMENT OF RUSSIAN-LANGUAGE COMMENTS OF SOCIAL NETWORK USERS
A.N. Zhdanova1, A.V. Kupriyanov1,2, A.A. Golova1, A.S. Bulgakov1, D.S. Bakanov1
1Samara National Research University, Samara, Russia
2Image Processing Systems Institute, Russian Academy of Sciences, Branch of the Federal State Research Center Crystallography and Photonics”, Russian Academy of Sciences, Samara, Russia
Keywords: sentiment analysis, recurrent neural networks, data analysis, text tonality, prediction
Abstract
The article is devoted to the application of machine learning methods for sentiment analysis of texts and to the study of the effectiveness of various architectures of neural networks. This direction is relevant in connection with the development of social networks and online recommendation services, where many users express their opinions about goods and services. The article presents the results of forecasting and comparing the structures of neural networks on real data from social networks. This allows determination of the most efficient architecture for the sentiment analysis of texts. The study may be useful for developers of social networks and recommendation services, as well as for researchers involved in natural language processing. The results can help improve the quality of the user opinion analysis and improve user satisfaction with services and products. Thus, the article contributes to the development of the field of machine learning and text data analysis.
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