FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis

Abstract

Sentence-level sentiment analysis is a natural language processing model growing rapidly and strongly due to its role in artificial intelligence systems. There are many approaches to developing and improving the performance of sentence-level sentiment analysis with satisfactory performance, among which deep neural network methods are notable. However, sentence-level sentiment analysis methods based on deep neural networks often have two limitations: (i) The system architecture is not deep enough; (ii) Sentences containing unclear sentiments cannot be processed. To solve the above two challenges, in this paper, we propose a new sentence-level sentiment analysis method called Fuzzy-enhanced Deep Neural Networks (FeDN2) by deepening deep neural networks by adding fuzzy and defuzzy classes. FeDN2 includes the following main layers: (i) BERT layer to convert sentences into vectors. (ii) Fuzzification layer to blur sentence vectors. (iii) Deep convolutional layers to extract high-level features. (iv) Defuzzification layer to convert high-level feature vectors into clear values. (v) Fully connected layer to learn non-linear combinations of these high-level features. (vi) Classification layer to identify the sentiment polarity of sentences. FeDN2 was tested on two benchmark datasets. The results demonstrated that it can improve the performance of previous sentence-level sentiment analysis methods based on deep neural networks.