1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg b
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1 1,a) , TPO Extracting Purpose-for-Action to Enhance Local Information Service Noriko Yokoyama 1,a) Kaname Funakoshi 1 Hiroyuki Toda 1 Yoshimasa Koike 1 Received: September 20, 2014, Accepted: January 5, 2015 Abstract: To suggest a target action and encourage the user to perform it, motivation and ability are necessary when a trigger is offered. However, traditional local information services focus on giving information related to the ability to perform the behavior (cost, place, etc.) and motivation is not considered; without effective motivation the target action is unlikely to be performed. Our goal is to identify an effective motivation approach. To achieve this goal, we collect purpose-for-action. In this paper, we propose a method to extract purpose-for-action from social media texts by using clue expressions and modification structure. Moreover, we conduct a user experiment whose results confirm that showing purpose-for-action yields effective motivation. Keywords: text mining, social media, purpose-for-action extraction, action support 1. *1 *2 *3 Foursquare *4 1 1 NTT NTT Service Evolution Laboratories, Yokosuka, Kanagawa , Japan a) yokoyama.noriko@lab.ntt.co.jp *1 *2 *3 *4 Foursquare c 2015 Information Processing Society of Japan 17
2 1 Fogg Fogg Behavior Model [1] information cascade [2] TPO [3] Fig. 2 Target area of this paper. 1 Fig. 1 Fogg behavior model Fogg behavior model. 3 Fig. 3 Example of application image. c 2015 Information Processing Society of Japan 18
3 4 Fig. 4 Extracting purpose-for-action from experience information (1) (2) (1) (2) (1) [4] [5] twitter LDA (2) [6], [7], [8], [9] [9] (1) (2) 2 (2) (2) (1) (2) 2.2 Foursquare information cascade Google *5 Amazon *6 [10] *5 *6 c 2015 Information Processing Society of Japan 19
4 Fig. 5 Definition of purpose-for-action Q&A [9] [11] 10 [12] (1) (2) 1 (1) (2) (3) c 2015 Information Processing Society of Japan 20
5 1 (1) (2) (1) (1) Table 1 Clue expressions extracted from blogs. 2 Table 2 Frequency of clue expressions [6] [6] Jdep [13] Fig. 6 Purpose-for-action extraction using clue expression and modification structure. c 2015 Information Processing Society of Japan 21
6 F [13] F 5 F = = 2 F = + BL2 BL1 2 BL Table 3 3 The number of correct examples BL1 [13] BL2 Table 4 4 The number of extracted examples. c 2015 Information Processing Society of Japan 22
7 5 Table 5 Extraction accuracy. Table 6 6 Initial improvement policies. BL1 1 F 2 2 FP: False Positive2 FP (1) (2) (3) (4) / (5) 5.1 FP Table 7 The change in the number of errors (close test). 8 Table 8 Removal of errors (open test). 7 62% 78% F 67% 70% BL2 BL2 BL2 F 8 F BL2 2 F 8 c 2015 Information Processing Society of Japan 23
8 9 Table 9 Clustering Table 10 Example of question FP P@Nc F ex (1) (2) (1) [11] 5 / // / [14] or x x > % c 2015 Information Processing Society of Japan 24
9 11 Table 11 Evaluation of the effect of system. 5W1H 2 12 Table 12 Examples of purpose-for-action scores (Running). + 7% 75% % [1] Fogg, B.J.: A Behavior Model for Persuasive Design, Proc. 4th International Conference on Persuasive Technology, Article No.40 (2009). [2] Bikhchandani, S., Hirshleifer, D. and Welch, I.: Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades, Journal of Economic Perspectives, Vol.12, No.3, pp (1998). [3] DEIM Forum 2012, F8-2 (2012). [4] FIT (2011). [5] Zhu, D., Fukazawa, Y., Karapetsas, E. and Ota, J.: Intuitive Topic Discovery by Incorporating Word-Pair s Connection into LDA, Proc IEEE/WIC/AIM International Conferences on Web Intelligence and Intelligent Agent Technology, pp (2012). [6] Vol.45, No.3, pp (2004). [7] Sakai, H. and Masuyama, S.: Cause information extraction from financial articles concerning business performance, IEICE Trans. Inf. Syst., E91-D(4), pp (2008). [8] Sakaji, H., Sakai, H. and Masuyama, S.: Automatic extraction of basis expressions that indicate economic trends, Proc. PAKDD 2008, pp (2008). [9] 14 pp (2008). [10] Adomavicius, G. and Tuzhilin, A.: Context-aware recommender systems, Recommender systems handbook, pp (2011). [11] 2013 (2013). [12] 24 7 (2012). [13] Imamura, K., Kikui, G. and Yasuda, N.: Japanese dependency parsing using sequential labeling for semispoken language, Proc. ACL, pp (2007). [14] (1977). 8. c 2015 Information Processing Society of Japan 25
10 NTT ACM ACM 1989 c 2015 Information Processing Society of Japan 26
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