Emotion Analysis of Literary Works Based on Attentional Mechanisms and the Fusion of Two-Channel Features

Abstract

In order to analyze sentiment data of foreign literary works, this paper proposes an algorithm for sentiment classification of literary works. We do this by fusing different features of literary works, which in turn captures more feature information for the classifier. As traditional word embedding models are difficult to achieve fusion with the sentiment features of literary works, we consider a multifeature fusion approach of word embedding features and lexical features of literary works. A two-channel and single-channel comparison is also used to analyze the classification accuracy based on the two feature fusion methods, and a parallel CNN and BiLSTM-attention two-channel neural network model proposed. Finally, the proposed model was evaluated using a real dataset of sentiment reviews of literary works and compared with different classification algorithms in the experiments. The experimental results show that the new hybrid approach has better classification accuracy, recall, and F1 metrics. The proposed methodology is an important guide for the creation of literary works and their screenplays, as it can be used to judge whether a work appeals to readers and, importantly, can also be considered as one of the criteria for the success of a film adaptation of a literary work.