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Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

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Understanding tourists’ perceptions of climate is essential to improving tourist satisfaction and destination marketing. This paper constructs a sentiment analysis framework for tourists’ perceptions of climate using not only continuous climate data but also short-term weather data. Based on Sina Weibo, we found that Chinese tourists’ perceptions of climate change were at an initial stage of development. The accuracies of word segmentation between sentiment and nonsentiment words using ROST CM, BosonNLP, and GooSeeker were all high, and the three gradually decreased. The positively expressed sentences accounted for 79.80% of the entire text using ROST EA, and the sentiment score was 0.784 at the intermediate level using artificial neural networks. The results indicate that the perceived emotional map is generally consistent with the actual climate and that cognitive evaluation theory is suitable to study text on climate perception.