Text Emotion Categorization Using a Convolutional Recurrent Neural Network Enhanced by an Attention Mechanism-based Skip Gram Method

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Text-based web archives have become increasingly common as technology has advanced. For many text classification applications, classic machine learning classifiers like support vector machines (SVMs) and naïve Bayes (NBayes) perform well. Since short texts have fewer words and convolutional and pooling layers have their limits, these classifiers suffer from sparsity and lack long-term dependencies. In this study, we present a convolutional recurrent neural network architecture that makes use of a modified skip-gram method. For the adversarial training of the skip-gram algorithm, we employ the L2 regularization technique. It can boost the model's performance in text sentiment classification tasks and increase its robustness and generalizability. To extract information from the entire text while dampening the influence of irrelevant words, we deployed a convolutional neural network equipped with attention mechanisms. The CNN-based categorization of text emotion is complete. When compared to other classifiers used on the Twitter dataset, our model and algorithm were shown to be more efficient and accurate.