Customer Emotion Analysis by Use of the Processing of Natural Languages using Deep Learning
Ramkumar Soundarapandian
Computer Science
Feburary 2024
The goal of this research is to combine deep learning methods with natural language processing (NLP) strategies to improve the precision and efficacy of customer emotion analysis. Deep learning and NLP ideas are used in content-based categorization to identify emotions in written documents. The present work presents deep learning assisted semantic text analysis (DLSTA) as a big data-driven approach for identifying customer emotions. Emotions in textual sources are recognised by utilising NLP concepts, and word embeddings—which are frequently employed in NLP activities—help with sentiment analysis, machine translation, and answering questions. Comparing the suggested method against many state-of-the-art techniques, numerical findings show that it obtains much better customer emotion detection rates of 98.24% and classification accuracy rates of 99.04% by using a variety of emotional word embeddings. The approach seeks to improve technical aspects of emotion analysis while expanding understanding of the complex relationships between linguistic expressions and clients' emotional states. This work has promise for the developing field of emotional computing as it creates a strong framework for improving the quality of customer emotion analysis through the synergistic combination of deep learning and natural language processing
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