A Deep Learning Based Emoticon Classification for Social Media Comment Analysis

Social Network plays a vital role in exchanging information in this smart world. In such situations, the usage of words is an important one to restrict the abusive information and text in the comment sections. This word usage was restricted by using text mining algorithms and classification techniques. But the word usage was reduced after the emoticons usage. Using emoticons also, the users can convey the harsh comments. As they combine both text and emoticons in their message, the identification of emoticons is important. Based on this, in this work, the identification of emoticons is performed using the Deep learning algorithm called Deep Neural network. Here, the emoticons from different groups were used as the input dataset. This emoticon was processed by using the proposed user defined convolutional neural network layer for emoticon classification. This emoticon classification performance will be analyzed to evaluation metrics. To enhance its accuracy further, the hyper parameters of proposed attention based DNN like learning rate and batch size will be tuned using Particle swarm algorithm. Then, its performance will be evaluated using evaluation metrics for identifying the best deep learning approach for emoticon classification. The whole process will be realized using MATLAB R2022a software.