Sentiment analysis by using Naïve-Bayes classifier with stacked CARU

A long sequence always contains long-term dependency problems, which leads to paragraph-based sentiment analysis being a very challenging task and difficult to evaluate by using a simple RNN network. It is proposed in this letter to use a stacked CARU network to extract the main information in a paragraph. The resulting network also points out how to use a CNN-based extractor to explore complete passages and capture useful features in their hidden state. In particular, instead of using the Softmax function, the Naïve-Bayes classifier is connected to the end of the CNN-based extractor. The proposed models also take into account the conditional independence of the observed results under the hidden variables, which aims to project features into a probability distribution appreciated for its simplicity and interpretability. The advantages of these models in sentiment analysis are empirically investigated by combining the usual classifiers with the results of GloVe embedding on the SST-5 and IMDB datasets.