Abstract
Broad adoption of smartphones has increased the number of posts generated while people are going about their daily lives. Many of these posts are related to the location where that post is generated. Being able to infer a person’s sentiment toward a given location would be a boon to market researchers. The large percentage of system-generated content in these posts posed difficulties for calculating sentiment and assigning that sentiment to the location associated with the post. Consequently our proposed system implements a sequence of text cleaning functions which was completed with a naive Bayes classifier to determine if a post was more or less likely to be associated with an individual’s present location. The system was tested on set of nearly 30,000 posts from Foursquare that had been cross-posted to Twitter which resulted in reasonable precision but with a large number of posts discarded.
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References
Bricker, E.: Can social media measure customer satisfaction? NetBase report, August 2011. (http://info.netbase.com/CanSocialMediaMeasureCustomerSatisfaction.html)
Banerjee, S., Viswanathan, V., Raman, K., Ying, H.: Assessing prime-time for geotargeting with mobile big data. J. Mark. Analytics 1(3), 174–183 (2013)
Diakopoulos, N., Shamma, D.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, p. 1195 (2010)
Cheng, Z., Caverlee, J., Lee, K., Sui, D.: Exploring millions of footprints in location sharing services. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp. 81–88 (2011)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1-2), 1–135 (2008)
Social Networking Fact Sheet. Internet: http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/ (27 December 2013)
Mostafa, M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)
Hodeghatta, U.: Sentiment analysis of Hollywood movies on Twitter. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1401–1404 (2013)
Zhang, Y., Chen, W., Yin, Z.: Collaborative filtering with social regularization for TV program recommendation. Knowl.-Based Syst. 54, 310–317 (2013)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project report, pp. 1–12 (2009)
Patil, S., Norcie, G., Kapadia, A., Lee, A.: Check out where I am! In: CHI 2012 Extended Abstracts on Human Factors in Computing Systems, pp. 1997–2002 (2012)
Cramer, H., Rost, M., Holmquist, L.: Performing a check-in: emerging practices, norms and “conflicts” in location-sharing using foursquare. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, pp. 57–66 (2011)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112 (2003)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, pp. 79–86 (2002)
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Languages in Social Media, pp. 30–38 (2011)
Bradley, M., Lang, P.: Affective norms for English words (ANEW): instruction manual and affective ratings (1999). http://a.parsons.edu/~spani621/thesis/context/ANEW.pdf (4 January 2014)
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Sanborn, R., Farmer, M., Banerjee, S. (2015). Assigning Geo-relevance of Sentiments Mined from Location-Based Social Media Posts. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_22
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DOI: https://doi.org/10.1007/978-3-319-24465-5_22
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