Wikipedia 2 Wikipedia Web Wikipedia 2. Web [6] [11] [8] 2 SVM Bollegala [1] 5-gram URL URL 2-gram [6] [11] SVM 3 SVM [8] Bollegala [1] SVM [7] [9] [6]

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1 DEIM Forum 2012 F Wikipedia SVM Abstract A study of Retrieval in Microblogging based on Person s Aliases Yutaro YAMAGUCHI, Satoshi SHIMADA, and Tetsuji SATOH College of Knowledge and Library Sciences, School of Informatics, University of Tsukuba 1 2 Kasuga, Tsukuba, Ibaraki, Japan Graduate School of Library, Information and Media Studies, University of Tsukuba 1 2 Kasuga, Tsukuba, Ibaraki, Japan {yamaguchi,satoh}@ce.slis.tsukuba.ac.jp, sat@slis.tsukuba.ac.jp In microblogging which the user can easily post comments intuitive, People are referenced in a variety of aliases other than personal names. Aliases is used in the tweets which reflect the context and user s feelings, it s not limited to mere means of referring to the person. In this paper, we propose the method to extract person s aliases using search engine and Wikipedia,and analyze topic and polarity of the article. Based on the result, we created the system which can retrieve context and polarity of the article in which the person s alias appear when user input a personal name. Key words Microblogging alias SVM 1. Twitter 1 Twitter Web 1http://twitter.com/

jp Wikipedia SVM Abstract A study of Retrieval in Microblogging based on Person s Aliases Yutaro YAMAGUCHI, Satoshi SHIMADA, and Tetsuji SATOH College of Knowledge and Library Sciences, School of

2 Wikipedia 2 Wikipedia Web Wikipedia 2. Web [6] [11] [8] 2 SVM Bollegala [1] 5-gram URL URL 2-gram [6] [11] SVM 3 SVM [8] Bollegala [1] SVM [7] [9] [6] Wikipedia Brendan [3] David [5] Aniket [4] K-means Affinity Propagation [2] 3 idf Affinity Propagation Web [7] Wikipedia Wikipedia Wikipedia [8] Wikipedia 1 2http://ja.wikipedia.org/ 3 4 Conference-Board) 6

SVM [7] [9] [6] Wikipedia Brendan [3] 4 0.

3 1 2 Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia [6] [11] alias fullname alias fullname 3 [6] alias fullname fullname alias fullname alias [11] alias fullname (1) fullname Web N (2) fullname 5 3 candidate fullname SVM SVM Support Vector Machine SVM SVM [11] SVM alias fullname 6 Dice(fullname, candidate) OverlapC(fullname, candidate) OverlapN(fullname,

fullname Web N (2) fullname 5 3 candidate fullname 3 5 3. 2 SVM SVM Support Vector Machine SVM SVM 3. 1.

4 candidate) 6 9 Dice(fullname, candidate) = Hits(fullname, candidate) Hits(name) + Hits(candidate) OverlapC(fullname, candidate) = Hits(fullname, candidate) Hits(candidate) OverlapN(fullname, candidate) = Hits(fullname, candidate) Hits(name) Hits(name, candidate) name AND candidate Hits(name) Hits(candidate) namecandidate (1) (2) (3) 8 SVM (1) Dice(fullname, candidate) (2) OverlapC(fullname, candidate) (3) OverlapN(fullname, candidate) (4) candidate candidate log(cf(candidate)) (5) candidate fn(candidate) (6) candidate fp(candidate) (7) candidate bn(candidate) (8) candidate bp(candidate) [10] 8,500 tweet pn(tweet) = posi nega posi + nega posi tweet nega tweet pn(tweet) ( 1.0 < = pn(tweet) < = 1.0) tweet (4) alias date pn alias(alias, day) pn alias(alias, date) = 1 n tweet T pn(tweet) (5) T date n T date pn alias(alias, date) candidate pn(tweet) pn alias(alias, date) 4 ( 1.0 < = pn alias(alias, day) < = 1.0) Wikipedia SVM F 5http://ja.wikipedia.org/wiki/Category:

OverlapC(fullname, candidate) (3) OverlapN(fullname, candidate) (4) candidate candidate log(cf(candidate)) (5) candidate fn(candidate) (6) candidate fp(candidate) (7) candidate bn(candidate) (8)

5 1 SVM SVM 2 fullname 6 fullname P recision = R N Recall = R C 2 precision recall F measure = precision + recall (6) :00: :00:00 geocode = , ,100km R SVM N SVM C Twitter Search API :00: :59:59 RT URL 2 3 RT URL SVM N 300 SVM LibSVM 6 SVM RBF C-SVC C gridsearch 9 excite 7 NAVER SVM 4. 2 Wikipedia Wikipedia 7 8 Wikipedia 1 4 false negative 5 false positive 1 Web http:// cjlin/libsvm/ 7http://tt.excite.co.jp/people/ 8http://person.naver.jp/issue

3 2 2 4 Twitter Search API 4 2011 8 27 0:00:00 9 20 23:59:59 RT URL 2 3 RT URL 224 70 346 313 3 3 11

6 faridyu Twitter Web Web URL Wikipedia Wikipedia 9 C Wikipedia Wikipedia Precision Recall F-measure Precision Recall F-measure

50 5 Wikipedia 824 464 80 6 Wikipedia 428 229 168 10 Precision Recall

7 5 : 7 : 6 : 8 : AKBINGO! pn alias pn alias jquery PHP MySQL DB Twitter API DB

8 SVM 0.83 [1] D. Bollegala, Y. Matsuo, and M. Ishizuka. Automatic discovery of personal name aliases from the web. Knowledge and Data Engineering, IEEE Transactions on, Vol. 23, No. 6, pp , june [2] Brendan J. Frey and Delbert Dueck. Clustering by passing messages between data points. Science, Vol. 315, pp , [3] Brendan O Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A.Smith. From tweets to polls:linking text sentiment to public oppinion time series. ICWSM-2010, [4] Aniket Rangrej, Sayali Kulkarni, and Ashish V. Tendulkar. Comparative study of clustering techniques for short text documents. 20th International World Wide Web Conference (WWW2011), p. 111, [5] David A. Shamma, Lyndon Kennedy, and Elizabeth F. Churchill. Peaks and persistence: modeling the shape of microblog conversations. Proceedings of the ACM 2011 conference on Computer supported cooperative work, pp , [6],. Web. (DBWS2006), [7],. blog. 18 (DEWS2007), [8],,,. Web. 19 DEWS2008, [9],,,,. Weblog. 22, [10],,.. 14, pp. pp , [11], Danushka Bollegala,,. Web. NLP 2,

[3] Brendan O Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A.Smith. From tweets to polls:linking text sentiment to public oppinion time series. ICWSM-2010, 2010.

DEIM Forum 2010 A Web Abstract Classification Method for Revie

DEIM Forum 2010 A Web Abstract Classification Method for Revie DEIM Forum 2010 A2-2 305 8550 1 2 305 8550 1 2 E-mail: s0813158@u.tsukuba.ac.jp, satoh@slis.tsukuba.ac.jp Web Abstract Classification Method for Reviews using Degree of Mentioning each Viewpoint Tomoya