An Approach to Using the mBERT Deep Learning Neural Network Model for Identifying Emotional Components and Communication Intentions
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2024
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The problem of using the mBERT deep learning neural network model for identifying emotional components and communication intentions was considered. For this purpose, a method of determining the emotional components and communication intentions of text messages using natural language processing tools was developed, which is capable of determining the emotional components and dominant emotion of a text message and forming an expert opinion regarding the communication intentions based on the determined dominant emotion with justification in the form of a list of emotionally colored words and phrases To solve the task of identifying emotional components from text messages, the mBERT model revealed several key advantages, the main one of which is a deep understanding of the context, thanks to bidirectional learning and a multi-headed attention mechanism. This allows to capture complex emotional connections between words in the text, even if the context depends on the order of the words. Another feature of the mBERT model is its high adaptability to new words thanks to the sequential segmentation of words into tokens. This is especially relevant when analyzing text messages from social networks, where informal vocabulary is mostly used.
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Blazhuk V., Mazurets O., Zalutska O. An Approach to Using the mBERT Deep Learning Neural Network Model for Identifying Emotional Components and Communication Intentions. The Impact of Scientific Research on the Development of the Modern World. Proceedings of the XLІV International scientific and practical conference. October 23-25, 2024. Dubrovnik, Croatia. 2024. Pp. 79-84