Sentiment analysis based on CNN - LSTM hotel reviews

Abstract

In order to obtain the implied semantic information of hotel reviews for emotional analysis, the correlation between discontinuous words is ignored in the traditional convolutional neural network (CNN) emotional analysis. Therefore, a novel sentiment analysis method based on CNN - LSTM model is proposed. In this method, CNN is used to extract semantic features from hotel review texts, and LSTM is used to add sentence structure features to enhance deep semantic learning. This model improves the accuracy and F1 value on the CHN senticorp-HTOL-BA-6000 hotel review data set, and can better solve the task of text sentiment analysis and discover the emotional orientation of text information.

Sentiment Analysis Of Hotel Reviews On Tripadvisor With LSTM And ELECTRA

“…In this study, the LSTM and ELECTRA models have been used to predict hotel reviews in Indonesian. (Yao, 2022) The results of the analysis show that the LSTM model has a fairly good performance, but is still less effective in predicting hotel reviews with an accuracy of 30%. Meanwhile, the ELECTRA model has better performance with an accuracy of 47%.…”