Sentiment Classification of Educational Tourism Reviews Based on Parallel CNN and LSTM with Attention Mechanism

Abstract

With the rapid development of the Internet and tourism, the Internet has been widely used in the tourism industry. Tourism enterprises and tourists use the Internet to publish and obtain travel-related information. Educational tourism is a new type of tourism activity. As a combination of “tourism + education,” it has gradually attracted the attention of tourists. With its convenience, fast speed, and low barrier, tourism text data provide great convenience for tourists’ sentiment calculation and have become one of the main sources of big data for tourism. However, the reviews of educational tourism have a lot of redundant information and complex sentence patterns, leading to a relatively low classification accuracy of the existing sentiment analysis algorithms. In order to effectively obtain the implicit semantic information of short text reviews for sentiment orientation recognition, a sentiment classification model for educational tourism online reviews based on parallel CNN and LSTM with multichannel attention mechanism is proposed. Firstly, Word2Vec technique is used, and based on noise word filtering, the feature words of educational tourism reviews are extracted to preprocess the input data set. Then, parallel CNN and LSTM are used to extract text local information and contextual features, and a multichannel attention mechanism is used to extract the attention values from the LSTM output. Finally, the output information of the multichannel attention mechanism is fused to effectively extract text features and focus on important words. The experimental results show that compared with other advanced methods, the proposed algorithm achieves improvements in terms of precision, recall, and F1 value and improves the AUC performance. It will help the educational tourism bases to carry out targeted development and construction in response to tourists feedback, enhance the sense of gain and happiness of tourists in educational tourism activities, improve tourism experience, and promote the rapid development of high-quality educational tourism.