Comparison and Applicability Study of Analysis Methods for Social Media Text Data: Taking Perception of Urban Parks in Beijing as an Example

The booming Internet technology and media have generated large sets of social media data, with which the social sensing analyses based on users' reviews have become a research hotspot and have been increasingly applied in the study of urban park usage and perception. However, most existing studies adopt a single model for text data processing. To fill this gap, this study aims to compare social media text data analysis methods and assess their advantages, disadvantages and applicability in park perception research. The Lexicon-based classification analysis model (lexicon model) and LDA (Latent Dirichlet Allocation) model widely used in relevant research were selected. Based on text data obtained from public reviews of 10 urban parks in Beijing on Dianping, this study explored the perception topic distribution of each park and all parks in general, and compared the classification results of perception topics between these two models. Results show that the lexicon model is conducive to the parallel comparison of perception frequency between parks, while the LDA model can directly reflect each park's characteristics and visitors' perception preferences; the combined use of the two models can optimize park perception assessment. Results from the two methods reveal that visitors to urban parks in Beijing focused more on their social recreation needs and visual aesthetics brought by the natural landscape, as well as conditions of the transportation facilities and the consumption in the parks. This research can provide optimization suggestions for the selection and use of social media text analysis methods, and a basis and guidance for park construction and management improvement.