Open AccessSentiment analysis by using Naïve‐Bayes classifier with stacked CARU
Author(s) -
Chan KaHou,
Im SioKei
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12478
Subject(s) - computer science , interpretability , softmax function , naive bayes classifier , sentiment analysis , artificial intelligence , classifier (uml) , extractor , machine learning , conditional independence , pattern recognition (psychology) , conditional probability , artificial neural network , data mining , mathematics , support vector machine , statistics , process engineering , engineering
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.