A Tourist Customer Comment and Feedback Emotion Analysis Model Based on Artificial Neural Network Algorithm

Text sentiment analysis has become a key technology in smart tourism. The tourism review text is lengthy and complex in sentence structure, and existing tourism sentiment analysis algorithms do not consider text characteristics and syntactic change rules, resulting in a decrease in classification accuracy. In order to enhance the reputation and competitiveness of tourist destinations such as scenic spots and hotels, how to obtain relevant data from massive user reviews and organize valuable information, and apply it to the construction of smart travel, has become a highly valued task for cultural and tourism authorities and tourism related enterprises. This article proposes a tourism customer comment and feedback sentiment analysis model based on artificial neural network (ANN) algorithms. Sentence features are extracted to obtain the sentiment classification results of comments. Subsequently, sentiment analysis is performed on the sentiment classification results to obtain user satisfaction with the scenic area. And the existing sentiment lexicon has been expanded to improve the accuracy of the model in sentiment classification in the tourism field. The experimental results show that the model proposed in this article has high accuracy in sentiment classification, can accurately capture user interest points, and improve the effectiveness of personalized recommendations.