Sentiment analysis by using Naïve-Bayes classifier with stacked CARU

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)411-413
Number of pages3
JournalElectronics Letters
Issue number10
Publication statusPublished - May 2022


Dive into the research topics of 'Sentiment analysis by using Naïve-Bayes classifier with stacked CARU'. Together they form a unique fingerprint.

Cite this