3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3
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1 Vol. 52 No (Dec. 2011) 1 1 Discovering Latent Solutions from Expressions of Dissatisfaction in Blogs Toshiyuki Sakai 1 and Ko Fujimura 1 This paper aims to find the techniques or goods that solve user s problems or dissatisfaction extracted in texts created by the user. We collected a large number of texts describing user experiences from blogs to extract expressions of dissatisfaction. These texts also contain information about the techniques or goods that solve the dissatisfaction and about their effectiveness. We found that the co-occurrence frequency of words that indicate problem prevention or solution, such as protect and cure, is an effective measure for realizing these extractions with high accuracy. We implemented a prototype system on the proposed method and tested it. The results show that the system can identify some useful goods for solving user s problems extracted in the texts. 1. SNS Twitter (1) (2) (3) (4) (1) (3) (1) Adwords 1) (2) Adsense 2) (3) Amazon 3) (4) (1) (3) 1 Twitter 1 NTT NTT Cyber Solutions Laboratories, NTT Corporation c 2011 Information Processing Society of Japan
2 3807 (3)(2) ,267 1 Fig. 1 Advertisement to the author of a blog. 3 (1) (2) (3) (2) (1) TV 2-0 Adsense (2) Web ) 6) 3 A A B B EM Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
3 3808 9) 3 7) EM 8) Web 10) 12) 12) pn 13) 13) pn { } { } 14) { } 15) 15) 15) 15) Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
4 { } { } 2 Fig. 2 System configuration of the proposed recommendation system (1) (1) ex. (2) ex. (3) ex. 1. A [] [] [ ] 2. B [] [] [ ] Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
5 ) 1 2 A A X X-1 web (1) B B (2) D (3) B V (4) B (2) D (3) v V P d (v) DF(d, v) d D P d (v) = (1) B DF(d, v) d, v (5) B M (3) v V P m(v) DF(m, v) m M P m(v) = (2) B DF(m, v) m, v (6) (4) (5) (3) (3) v P kai (v) P kai (v) =P d (v) P m(v) (3) (7) (6) (8) (9) (8) {} {} (1) ex. A (2) ex. A (3) ex. A (4) ex. A (1) (4) (2) (3) (1) (4) (2) (3) 2 2 Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
6 Fig. 3 Extraction of the relations between dissatisfactions and items. 3.2 X Y (1) (4) (2) (3)(4) (4) 3 { } D u u V 3.3 i d i d r(i, d) r(i, d) = DF(i, d, v) (4) v V DF(i, d, v) V i d u i score u(i) i I u score u(i) = Dic(r(i, d)) (5) d D u rank 1 rank 1 Dic (r(i, d)) Dic d Dic i r(i, d ) d score u(i) u i i d D u Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
7 ,281,765 JTAG 17) (3) { } { } goo 18) WiiFitWiiFit Wii 46, byte 1 1 (1) (2) Table 1 Extracted kaizen-dousa words that indicate problem prevention or solution. P kai e e e e e e e e e e-04 2, Table 2 Examples of the extracted dissatisfactions Table 3 Relations extracted by frequency of Table 4 Relations extracted by frequency of co-occurance (without kaizen-dousa ). co-occurance (with kaizen-dousa ) {} 10 { } (1) (2) Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
8 Fig. 4 Accuracy of extracted relation (the range of co-occurance is within a blog). A solid line represents accuracy without kaizen-dousa, and a dotted line represents accuracy with kaizendousa. (1) (2) 3 WiiFit WiiFit Fig. 5 Accuracy of extracted relations (with kaizen-dousa, the range of co-occurance is changed). 5 6 Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
9 3814 Fig. 6 6 Recall of the extracted relations { } 35, Fig. 7 Output of the system. 5 5 Table 5 Output of the system (top 5). rank rank Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
10 web ) Adwords, available from 2) Adsense, available from 3) Amazon, available from 4) Vol.48, No.9, pp (2007). 5) Jin, X., Zhou, Y. and Mobasher, B.: A Maximum Entropy Web Recommendation System: Combining Collaborative and Content Features, Proc. ACM SIGKDD Conf., pp (2005). 6) Popescul, A., Ungar, L.H., Pennock, D.M. and Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, UAI-2001 (2001). 7) Berger, A.L., Della Pietra, S.D. and Della Pietra, V.J.D.: A maximum entropy approach to natural language processing, Computational Linguistics, Vol.22, No.1, pp (1996). 8) Dempster, A., Laird, N. and Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. B, Vol.39, pp.1 38 (1977). 9) Vol.19, No.3a (2004). 10) Hatzivassiloglou, V. and McKeown, K.R.: Predicting the semantic orientation of adjectives, ACL, pp (1997). 11) pp (2004). 12) 10 (2004). 13) 14 pp (2008). 14) De Saeger Stijn 14 Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
11 3816 pp (2008). 15) Web NLP2008 (2008). 16) (2007). 17) Fuchi, T. and Takagi, S.: Japanese morphological analyzer using word cooccurrence JTAG, Proc. COLING-ACL 98: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Montreal, Vol.1, pp (1998). 18) goo, available from ( ) ( ) NTT 2007 NTT 1989 Vol. 52 No (Dec. 2011) c 2011 Information Processing Society of Japan
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