Semi-supervised dimensional sentiment analysis with variational autoencoder

Abstract Dimensional sentiment analysis (DSA) aims to compute real-valued sentiment scores of texts in multiple dimensions such as valence and arousal. Existing methods for DSA are usually based on supervised learning. However, it is expensive and time-consuming to annotate sufficient samples for training. In this paper, we propose a semi-supervised approach for DSA based on the variational autoencoder model. Our model consists of three modules: an encoding module to encode sentences into hidden vectors, a sentiment prediction module to predict the sentiment scores of sentences, and a decoding module that takes the outputs of the preceding two modules as input and reconstructs the input sentences. In our approach, the sentiment prediction module is encouraged to accurately predict sentiment scores of both labeled and unlabeled texts to help the decoding module reconstruct such texts more accurately. Thus, our approach can exploit useful information in unlabeled data. Experimental results on three benchmark datasets show that our approach can effectively improve the performance of DSA with considerably less labeled data.