Analyzing Tourism Information on Twitter for a Local City

Abstract

Tourism for a local city is one of the most important key industries. The Web contains much information for the tourism, such as impressions and sentiments about sightseeing areas. Analyzing the information is a significant task for tourism informatics. In this paper, we propose a tourism information analysis system for a local city. The target resource for the analysis is information on Twitter. First, we discuss a method to extract tweets (posted sentences) relating to the target locations and tourism events. Then, we analyze the polarity of the extracted tweets; positive or negative opinions. It is well-known as a P/N classification task in natural language processing. For the process, we employ an unsupervised machine learning approach that uses seed words. We evaluate and consider the extraction and P/N classification tasks. The experimental result about P/N classification shows the effectiveness of our method.