{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:30:38Z","timestamp":1754155838533,"version":"3.41.2"},"reference-count":16,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2018,4,3]],"date-time":"2018-04-03T00:00:00Z","timestamp":1522713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2018,4,3]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to propose a method for summarizing the topics of tweets using the Wikipedia category structure as common knowledge for supplementing the understanding of the Twitter user\u2019s interests. There are many topics in the tweets, and the topics can be treated as a tree structure. However, when the topic hierarchy is constructed using existing hierarchal clustering approach, the granularity of tweet groups differs for each user. For summarizing the topics, identification of the topics which are heterogeneous and which are not is necessary because it is easy to understand if several groups are categorized into parent groups. However, if the group units are different for each user, a number of users\u2019 interests cannot be summarized. If some tweets are grouped into the presidential election, and the others are into Donald Trump, there cannot be a count of how many users are interested in Donald Trump.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>One solution of this issue is to construct topic structures by mapping one common tree structure. In this paper, a method is proposed for constructing the topic structure using the Wikipedia category tree similar to a common tree structure. The tweets are categorized, mapped to titles of articles in the Wikipedia category tree and then visualized as the hierarchal structure to the users.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The effectiveness of the proposed hierarchal topic structure is confirmed. In theme \u201cpolitics\u201d, the proposed method works well. The main reason is that there are many technical terms about politics in the Wikipedia categories and articles. It was found that a number of the terms of politics do not have multiple meanings, multiple semantics. However, in theme \u201csports\u201d, the proposed method does not perform well. The main reason for this case is that there are a number of names of people present as topic names.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>One important feature of the proposed method is that it is easy to grasp not only about the topics which are heterogeneous or homogeneous with each other but also consider the missing time when extracting topics. Another feature is that the topic structures for multiple users are easy to compare with each other.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-d-18-00008","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T08:32:26Z","timestamp":1525249946000},"page":"2-14","source":"Crossref","is-referenced-by-count":0,"title":["What is your tweet worldview? Mapping the topic structure of tweets on the Wikipedia"],"prefix":"10.1108","volume":"14","author":[{"given":"Yu","family":"Suzuki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiromitsu","family":"Ohara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akiyo","family":"Nadamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020092920033102200_ref001","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"first-page":"4:1","article-title":"Emerging topic detection on Twitter based on temporal and social terms evaluation","year":"2010","key":"key2020092920033102200_ref002"},{"issue":"204","key":"key2020092920033102200_ref003","first-page":"47","article-title":"Clustering for similar recipes by using cooking ingredient","volume":"114","year":"2014","journal-title":"IEICE Technical Report"},{"first-page":"80","article-title":"Empirical study of topic modeling in Twitter","year":"2010","key":"key2020092920033102200_ref004"},{"year":"2000","key":"key2020092920033102200_ref005","article-title":"A comparison of document clustering techniques"},{"first-page":"745","article-title":"Emerging topic detection using dictionary learning","year":"2011","key":"key2020092920033102200_ref006"},{"first-page":"1155","article-title":"TwitterMonitor: trend detection over the Twitter stream","year":"2010","key":"key2020092920033102200_ref007"},{"first-page":"73","article-title":"Discovering users\u2019 topics of interest on Twitter: a first look","year":"2010","key":"key2020092920033102200_ref008"},{"first-page":"891","article-title":"Navigating the topical structure of academic search results via the Wikipedia category network","year":"2011","key":"key2020092920033102200_ref009"},{"first-page":"215","article-title":"Followee recommendation based on topic extraction and sentiment analysis from tweets","year":"2015","key":"key2020092920033102200_ref010"},{"first-page":"439","article-title":"Collective knowledge ontology user profiling for Twitter \u2013 automatic user profiling","year":"2013","key":"key2020092920033102200_ref011"},{"first-page":"1977","article-title":"Online topic model for Twitter considering dynamics of user interests and topic trends","year":"2014","key":"key2020092920033102200_ref012"},{"first-page":"841","article-title":"Short text classification in Twitter to improve information filtering","year":"2010","key":"key2020092920033102200_ref013"},{"article-title":"A comparison of document clustering techniques","volume-title":"6th ACM SIGKDD, World Text Mining Conference","year":"2000","key":"key2020092920033102200_ref014"},{"first-page":"327","article-title":"Topical semantics of Twitter links","year":"2011","key":"key2020092920033102200_ref015"},{"year":"2011","key":"key2020092920033102200_ref016","article-title":"Comparing Twitter and traditional media using topic models"}],"container-title":["International Journal of Pervasive Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJPCC-D-18-00008\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJPCC-D-18-00008\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:06:01Z","timestamp":1753394761000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijpcc\/article\/14\/1\/2-14\/161244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,3]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,4,3]]}},"alternative-id":["10.1108\/IJPCC-D-18-00008"],"URL":"https:\/\/doi.org\/10.1108\/ijpcc-d-18-00008","relation":{},"ISSN":["1742-7371"],"issn-type":[{"type":"print","value":"1742-7371"}],"subject":[],"published":{"date-parts":[[2018,4,3]]}}}