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Accurate emotion classification for online reviews is vital for businessorganizations to gain deeper insights into markets. Although deep learning hasbeen successfully implemented in this area, accuracy and processing time arestill major problems preventing it from reaching its full potential. This paperproposes an Enhanced Leaky Rectified Linear Unit activation and Weighted Loss(ELReLUWL) algorithm for enhanced text emotion classification and fasterparameter convergence speed. This algorithm includes the definition of theinflection point and the slope for inputs on the left side of the inflectionpoint to avoid gradient saturation. It also considers the weight of samplesbelonging to each class to compensate for the influence of data imbalance.Convolutional Neural Network (CNN) combined with the proposed algorithm toincrease the classification accuracy and decrease the processing time byeliminating the gradient saturation problem and minimizing the negative effectof data imbalance, demonstrated on a binary sentiment problem. The results showthat the proposed solution achieves better classification performance indifferent data scenarios and different review types. The proposed model takesless convergence time to achieve model optimization with seven epochs againstthe current convergence time of 11.5 epochs on average. The proposed solutionimproves accuracy and reduces the processing time of text emotionclassification. The solution provides an average class accuracy of 96.63%against a current average accuracy of 91.56%. It also provides a processingtime of 23.3 milliseconds compared to the current average processing time of33.2 milliseconds. Finally, this study solves the issues of gradient saturationand data imbalance. It enhances overall average class accuracy and decreasesprocessing time.

Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss