Analysis of Diverse Tourist Information Distributed Across the Internet

Herein, we propose and discuss a new method for analyzing various types of tourist information about the Suwa area of Nagano Prefecture, Japan, available on the Internet. This information includes not only long sentences that can be found on web pages and in blogs, but also short sentences comprising a few words posted on social media. In this paper, we propose a novel method based on a neural network, called paragraph vector, for expressing relationships between words included in sentences. Our method achieves high retrieval accuracy even across social media posts comprising just a few words. Based on our evaluation results, the proposed method outperforms the conventional information retrieval technique wherein sufficient accuracy cannot be achieved as it is based on the occurrence probability of words in sentences. This improvement is achieved by using the word order as an input feature to the neural network model.