Exploring Neighborhood Service and Development Strategies by Multi-dimensional Sentiment Analysis of Online Restaurant Review

With the rise of the Internet economy, cyber data have interacted with the physical world. One of the directions is to merge attribute and qualitative data with location information. In this paper, we apply the sentiment analysis of online restaurant reviews based on the public data from Meituan and Weibo, the two most influential online platforms in China, to study the restaurant facility in the neighborhood from the perspective of users, and look for neighborhood services' management and development strategies in the big data background. This paper takes Shanghai as an example, establishing a multi-dimensional evaluation model to study Shanghai's restaurant facilities from seven dimensions, which are obtained and categorized from a bottom-up method. Different from the “quantity” analysis in previous studies of neighborhood facilities, this paper pays more attention to the “quality” analysis of neighborhood facilities from the perspective of residents. Besides, this paper also enriches the empirical study of bidirectional LSTM sentiment analysis, which will promote the development of restaurant business and neighborhood services, and contribute to both the economic benefits of enterprises and refined governance of cities.