Live Comments Emotional Analysis based on EE-RNN

Live comments, also known as video Danmaku, is a technique through which audiences can express their real-time feelings and opinions with rich emotional information. Emotional analysis results of live comments can more truly reflect the overall characteristics of the video, while user's feedback can be further exploited by extensive applications. Most of the existing live comments emotion classification methods do not fully consider either the real fine granularity or the explicit emotional knowledge of the on-screen comments text. Besides, existing machine learning methods and deep learning methods such as Long Short-Term Memory neural network and Convolutional Neural Network based models do not make full use of the semantic layer representation and emotional features of the text. In this paper, Enhanced ERNIE Deep Recurrent Neural Networks model (EE-RNN) is employed to complete the five-dimensional live comments emotional analysis. The model first obtains the general semantic embedding of the text through ERNIE and introduces external emotional knowledge to further enhance the semantic coding representation, and then uses improved RNN structure as well as attention mechanism to get an emotional enhanced high-level semantic feature representation. Experimental results on the live comments emotional classification dataset and NLPCC2014 emotional classification dataset show that the proposed model greatly improves the classification performance compared with the existing methods and can be used in real applications.