Sentiment Analysis of Hotel Reviews Based on Deep Learning

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Many hotel evaluations have been written and shared online these days. Machine learning sentiment classification requires complicated artificial design features and feature extraction technique, whereas emotion dictionary-based sentiment classification requires a large amount of emotional database resources. In this study, we present the idea of long short-term memory. The text categorization method is used to determine the general tone. First, the brief comment text is processed into the LSTM network using word2vec and word segmentation technology; next, a dropout technique is implemented to avoid overfitting in order to get the final rating model. By using the LSTM network's superior short-term memory, a positive impact has been realized on sentiment categorization of reviews of hotels, with a precision of more than 95%.