An integrated fuzzy neural network with topic-aware auto-encoding for sentiment analysis

Abstract

Recent advanced deep learning architectures, such as neural seq2seq and transformer, have demonstrated remarkable improvements in multi-typed sentiment classification tasks. Even though recent transformer-based and seq2seq-based models have successfully enabled to capture rich contextual information of texts, they still lacked attention on incorporating global semantic information which enables to sufficiently leverage the performance of downstream SA tasks. Moreover, emotional expressions of users are normally in the form of natural human-written textual data which contains a lot of noises and ambiguities that impose great challenges on the processes of textual representation learning as well as sentiment polarity prediction. To meet these challenges, we propose a novel integrated fuzzy neural architecture with a topic-driven textual representation learning approach for handling the SA task, called as: TopFuzz4SA. Specifically, in the proposed TopFuzz4SA model, we first apply a topic-driven neural encoder–decoder architecture with the incorporation of latent topic embedding and attention mechanism to sufficiently learn both rich contextual and global semantic information of the given textual data. Then, the achieved rich semantic representations of texts are fed into a fused deep fuzzy neural network to effectively reduce the feature ambiguity and noise, forming the final textual representations for sentiment classification task. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed TopFuzz4SA model compared with contemporary state-of-the-art baselines.