Lexical Methods for Identifying Emotions in Text Based on Machine Learning

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The study of emotions has emerged as an important area of research because of the wealth of information it can provide. Emotions can be expressed in a variety of ways including words, facial expressions, written material, and movements. Natural language processing (NLP) & deep learning concepts are essential to solving the content-based classification problem that is emotion detection in a text document. Therefore, in this research, we suggest using deep learning to aid semantic text analysis in the task of identifying human emotions from transcripts of spoken language. Visual forms of expression, such as makeover jargon, may be used to convey the feeling. Datasets of recorded voices from people with Autism Spectrum Disorder (ASD) are transcribed for analysis. However, in this paper, we specialize in detecting emotions from all of the textual dataset and using the semantic data enhancement process to fill a few of the phrases, or half-broken speech, as patients with Autism Spectrum Disorder (ASD) lack social contact skills due to the patient not very well articulating their communication.