Sentiment Analysis by Hierarchical Deep Neural Networks for Audience Opinion Mining

The prominent applications of sentiment analysis encompass various fields, including marketing, customer service, and communication. The conventional bag-of-words approach for measuring sentiment only counts term frequencies, while neglecting the position of the terms within the discourse. As a remedy, this research aims to build a discourse-aware approach upon the discourse structure of documents. For this purpose, rhetorical structure theory (RST) is utilized to label (sub-) clauses according to their hierarchical relationships, and then polarity scores are assigned to individual leaves. To learn from the resulting rhetorical structure, a hierarchical category structure-based deep recurrent neural network is proposed to infer underlying tensors of salient passages of narrative materials to process the complete discourse tree. The significance of this study lies in enhancing the structure of exploding and vanishing gradients in deep recurrent neural networks and also improving evaluation criteria in text analysis using the structure of opinion mining. The proposed approach is RST-HRNN. Exploding gradients is a process in the backpropagation stage aimed at continuously sampling the gradient of the model parameter in the opposite direction based on the weight (w), which is continuously updated until reaching the minimum global function.