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 par...

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Bibliographic Details
Published inElectronics letters Vol. 58; no. 10; pp. 411 - 413
Main Authors Chan, Ka‐Hou, Im, Sio‐Kei
Format Journal Article
LanguageEnglish
Published Stevenage John Wiley & Sons, Inc 01.05.2022
Subjects
Artificial intelligence
Classification
Data mining
Design
Extractors
Language
Machine learning
Natural language
Neural networks
Random variables
Sentiment analysis
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Summary: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.
ISSN:0013-5194
1350-911X
DOI:10.1049/ell2.12478