The influence of foreign tourists on the Japanese economy is significant. However, the number of tourists have grown at a sluggish pace in local region. In local region, there are attractive features not found in urban areas, and there is possibility that hidden information can be transmitted in addition to the existing famous tourist information. Therefore, there is a demand from tourist operators to discover the potential needs of their area.
In this paper, we propose a method for analyzing objects that the area has given interest to visitors from images posted on the social networking service to discover potential needs in the region. The feature of this method is that it uses an image analysis service based on deep learning opened on the cloud service. This image analysis service can recognize the objects in a image and output their names as tag information. Therefore, by collecting SNS's images in any region, converting then into tag information, and statistically analyzing the tag information, the objects that the region is interested in is extracted as tag information. In this paper, we propose a analysis method of tag information based on the frequency of appearance tags and based on the difference between appearance tags in other regions. And the effectiveness was verified through the several experiments. In general, deep learning technology requires collection of large amount images and labeling for learning data in order to construct an image recognition model. In addition, in order to learn the model, it is necessary to prepare high spec computer environment. When introducing the analysis system of SNS's images, it is difficult to request them from tourist operators. On the other hand, the model of cloud service will be updated year by year, and the accuracy and versatility will be improved. Therefore, the effectiveness of using cloud services to analyze SNS's images is clarified in this paper.
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