Natural Language Processing and Complex Network based Tourism Social Big Data Analysis

Wuhan Business School, China

Abstract

Unlike traditional research methods such as questionnaire surveys and individual interviews, this article proposes a big data based analytical framework for tourism research. Taking the study of tourists’ image on Wuhan's Yangtze River culture as a example, we first utilize web crawler technology to obtain online comments from tourists. Then, the key words and perceptions on Yangtze River culture are identified through natural language processing (NLP). Finally, we adopt complex networks, grounded theory and other methods, to evaluate tourists' perception and analyze influencing factors. The proposed framework provides a comprehensive understanding on the tourism social data and the corresponding results show that tourists show positive emotional expressions towards the Yangtze River cultural tourism in Wuhan, which mainly manifested in its high landscape value, cultural connotations, overall environment, and tourism effects.