The information of social media is not often written in ordinary web pages. Nevertheless, it is difficult to extract such information from social media because such services include so much information. Furthermore, various topics are mixed in social media communities. The authors designate such important and unique information related to social media as tip information. In this paper, they aim to propose a method to extract tip information that has been classified by topic from social networking services as a first step in extracting tip information from social media.
Themes of many kinds exist in a social media community because users write contents freely. Then the authors first detect the topics from the community and cluster the comment based on the topics. Subsequently, they extract tip information from each cluster. In this time, the tip information is include a user's experience and it has common important words.
The authors used an experiment to confirm that their proposed method can extract appropriate tip information from a community that a user specifies. The average precision is 69 per cent. A comparison of the authors' proposed method and baseline which is without detection of topic and clustering, the average precision obtained using the authors' proposed method is 18 per cent greater than the baseline.
The authors have three points to extract tip information from social media: topic detection and clustering from the social media using LDA method; extracting an author's actual experiences; and creation of a tip keyword dictionary from user experiments.
Hattori, Y. and Nadamoto, A. (2013), "Tip information from social media based on topic detection", International Journal of Web Information Systems, Vol. 9 No. 1, pp. 83-94. https://doi.org/10.1108/17440081311316406Download as .RIS
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