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Wildlife Tourism Experience Based on Web Text Analysis
Author(s) -
Yiran Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1574/1/012144
Subject(s) - tourism , wildlife , wildlife tourism , the internet , content analysis , geography , destinations , advertising , ecotourism , marketing , world wide web , sociology , computer science , business , ecology , social science , archaeology , biology
With the improvement of living standard, people are becoming more and more interested in traveling, especially to visit wild animals. How to analyze the tourism experience of tourists visiting wild animals has become a research hot spot. The text comments left by tourists on the Internet can provide relevant information, so it is essential to carry out the research on the tourism experience of wild animals based on the text analysis on the Internet. The purpose of this paper is to solve the problem on how to understand the tourism wild animals after the visit of the tourism experience problems, by studying the current common network text analysis method, the tourists leave comments on the Internet information extraction, using relevant software comprehensive analysis of relevant information, using content analysis and qualitative analysis of a combination of both sexual themes, on how to analyze tourism personnel to visit wildlife tourism experience has carried on the detailed analysis and research. The tourism experience of wildlife tourists is analyzed in detail and accurately. The results show that the core themes of wildlife tourism experience are tourists, destinations and wildlife. When tourists visit wild animals, they are more likely to take photos and get close contact with them. They are closer to wild animals and interact with them more, which means that tourists are more satisfied with their tourism experience. This web-based text analysis method is 20% more efficient than traditional methods such as questionnaires.

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