Neural network approach for emotional recognition in text

Nazarenko, D. S.; Afanasieva, I. V.; Golian, N. V.

Публікація:
Neural network approach for emotional recognition in text

dc.contributor.author	Nazarenko, D. S.
dc.contributor.author	Afanasieva, I. V.
dc.contributor.author	Golian, N. V.
dc.date.accessioned	2021-11-28T13:55:52Z
dc.date.available	2021-11-28T13:55:52Z
dc.date.issued	2019
dc.description.abstract	The article is devoted to one of the most popular trends in the field of IT today – natural language processing, in particular, the extraction of emotions from the text using the neural network approach. The main task was to solve the problem of the high costs of time and human resources for companies to receive feedback from users and process emotional reactions of the second one. That to decide the task it was necessary to make modelling and learn neural network using own architecture based on the backpropagation algorithm that to recognize the emotional component in the text.The emotional component of reviews was used as a metric for evaluating user reactions. It was decided to work with five types of emotions that will help to provide better results. The neural network architecture consists of interconnected layers: embedding, bidirectional LSTM, pooling, dropout layers and two dense layers. For the neural network learning was selected an open dataset consisted of 47,288-tagged posts from Twitter. As a result, the F-measure on the test dataset was 0.62 and which is a worthy indicator in comparison with large business solutionsю	uk_UA
dc.identifier.citation	Nazarenko D. S. Neural network approach for emotional recognition in text / D. S. Nazarenko, I. V. Afanasieva, N. V. Golian // Бионика интеллекта : научно-технический журнал. – 2019. – № 1 (92). – С. 9–13.	uk_UA
dc.identifier.uri	https://openarchive.nure.ua/handle/document/18412
dc.language.iso	en	uk_UA
dc.publisher	ХНУРЕ	uk_UA
dc.subject	classification	uk_UA
dc.subject	data	uk_UA
dc.subject	emotions	uk_UA
dc.subject	neural networks	uk_UA
dc.subject	responses	uk_UA
dc.subject	tonality	uk_UA
dc.title	Neural network approach for emotional recognition in text	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication