Abstract
Twitter contains a large number of postings related to the reputation of products and services. Analyzing these data can provide useful marketing information. Inferring the user class would make it possible to extract opinions related to each class. In this paper, we propose a method that treats each user’s posting location for a tweet as a feature in the analysis of user classes. The proposed method creates clusters of geotags (obtained from Twitter tags) to identify the locations most often visited by the target user, which are then used as features. As an example, we conducted experiments to classify targets based on three classes: “student,” “working member of society,” and “housewife.” We obtained an average F-measure of 0.779, which represents an improvement on baseline results.
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References
Brdar, S., Ćulibrk, D., Crnojević, V.: Demographic attributes prediction on the real-world mobile data. In: Proceedings of the Mobile Data Challenge by Nokia Workshop in conjunction with International Conference on Pervasive Computing, Newcastle, UK, June 2012
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM2010), Toronto, ON, Canada, pp. 759–768, October 2010
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), Portland, OR, USA pp. 226–231, August 1996
Gao, H., Tang, J., Liu, H.: Mobile location prediction in a spatio-temporal context. In: Proceedings of the Mobile Data Challenge by Nokia Workshop in conjunction with International Conference on Pervasive Computing, Newcastle, UK, June 2012
Kinsella, S., Murdock, V., O’Hare, N.: “I’m eating a sandwich in glasgow": modeling locations with tweets. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents (SMUC 2011), Glasgow, UK, pp. 61–68, October 2011
Kudo, T., Yamamoto, K., Matsumoto, Y.: Applying conditional random fields to Japanese morphological analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain, pp. 230–237, April 2004
Lee, J., Ahn, J., Oh, J.S., Ryu, H.: Mysterious influential users in political communication on Twitter: user’s occupation information and its impact on retweetability. In: Proceedings of the iConference 2015, Newport Beach, CA, USA, March 2015
Manning, C.D., Raghavan, P., Schuetze, H.: Introduction to Information Retrieval, pp. 272–275. Cambridge University Press, Cambridge (2008). Chap. 13.5.1
Narayanan, V., Arora, I., Bhatia, A.: Fast and accurate sentiment classification using an enhanced naive Bayes model. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 194–201. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41278-3_24
Otterbacher, J.: Inferring gender of movie reviewers: exploiting writing style, content and metadata. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), Toronto, Canada, pp. 369–378, October 2010
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Preoţiuc-Pietro, D., Lampos, V., Aletras, N.: An analysis of the user occupational class through twitter content. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China, pp. 1754–1764, July 2015
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents (SMUC 2010), Toronto, ON, Canada, pp. 37–44, October 2010
Schler, J., Koppel, M., Argamon, S., Pennebaker, J.: Effects of age and gender on blogging. In: Proceedings of the AAAI Spring Symposium Computational Approaches to Analyzing Weblogs, Menlo Park, CA, USA, pp. 191–197, March 2006
Ye, M., Janowicz, K., Mülligann, C., Lee, W.C.: What you are is when you are: the temporal dimension of feature types in location-based social networks. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, USA, pp. 102–111, November 2011
Zamal, F.A., Liu, W., Ruths, D.: Homophily and latent attribute inference: inferring latent attributes of twitter users from neighbors. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012), Palo Alto, CA, USA, pp. 387–390, June 2012
Acknowledgment
This work was partially supported by a JSPS Grant-in-Aid for Scientific Research (B) (#16H02913).
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Takeda, N., Seki, Y. (2016). Twitter User Classification with Posting Locations. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham. https://doi.org/10.1007/978-3-319-49304-6_35
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