Identification of Text Emotions Through the Use of Convolutional Neural Network Models

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Increasing numbers of people are using the Internet to share their feelings and communicate with one another, and the vast majority of these expressions of emotion take the form of text. Using sentiment dictionaries, machine learning, and deep learning are the three most common approaches to text sentiment categorization studies. Due to the exponential growth of textual data, it is crucial to create models that can automatically analyse this material. Labels like gender, age, nationality, emotion, etc., may be included in the texts. Numerous investigations of text categorization have emerged because the use of such labels may be useful in various commercial sectors. The Convolutional Neural Network, also referred to as CNN, was recently utilised to the problem of text categorization, with promising results. In this study, we advocate for the use of convolutional neural network networks for the job of classifying emotions. Using three popular datasets, we demonstrate that our networks outperform existing cutting-edge deep learning models by using successive convolutional layers to process substantially longer sentences.