With the win-win development of tourism and the Internet, word-of-mouth ranking of tourist attractions is a valuable reference factor. We try to find a correlation between tourist reviews and taste ranking of tourist attractions. We study the sentiment features of tourist online reviews from the technical perspective of natural language processing, so we propose an improved long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We abandon traditional dictionaries and machine learning methods. A deep neural network approach was chosen to decompose multisentiment travel reviews into different morpheme levels for classification. Then, through preprocessing, text sentiment topic detection, and sentiment classification network, an accurate grasp of the sentiment features of reviews is finally achieved. To test the performance of our method, we built a web review database by crawler for experimental validation. Experimental results show that our method maintains more than 90% accuracy in comment sentiment detection, significantly outperforming dictionary methods and machine learning methods.
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