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On-Site Likelihood Identification of Tweets Using a Two-Stage Method

Part of the Intelligent Systems Reference Library book series (ISRL,volume 90)


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. A useful target resource for the analysis is information on Twitter. However, all tweets with keywords, which are related to facilities and events for tourism, might not be tourism information. In this paper, we propose a method for estimating on-site likelihood of tweets. The task is to identify whether each tweet has high on-site likelihood or not. We introduce a filtering process and a machine learning technique for the task. In addition, we apply previous and next tweets for the identification task, as context information. Experimental results show the effectiveness of the combination method and context information.


  • On-site likelihood
  • Tweet
  • Rule
  • Context

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Fig. 6.1
Fig. 6.2
Fig. 6.3
Fig. 6.4


  1. 1.

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  3. 3.

  4. 4.

    It is a famous temple in Kyoto.

  5. 5.

    E.g., the 1st and 3rd tweets of User D in Fig. 6.3.

  6. 6.

    It is \(721+(3114-1337)\) in Table 6.1.

  7. 7.

    In this case, the proposed method sacrificed the recall rate of 4.4 % in the filtering process.


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Correspondence to Kazutaka Shimada .

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Shimada, K., Onitsuka, Y., Inoue, S., Endo, T. (2015). On-Site Likelihood Identification of Tweets Using a Two-Stage Method. In: Matsuo, T., Hashimoto, K., Iwamoto, H. (eds) Tourism Informatics. Intelligent Systems Reference Library, vol 90. Springer, Berlin, Heidelberg.

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