ABSTRACT
Location-based services, and in particular personal navigation systems, have become increasingly popular with the widespread use of GPS technology in smart devices. Existing navigation systems are designed to suggest routes based on the shortest distance or the fastest time to a target. In this paper, we propose a new type of route navigation based on regional context---primarily sentiments. Our system, called SocRoutes, aims to find a safer, friendlier, and more enjoyable route based on sentiments inferred from real-time, geotagged messages from Twitter. SocRoutes tailors routes by avoiding places with extremely negative sentiments, thereby potentially finding a safer and more enjoyable route with marginal increase in total distance compared to the shortest path. The system supports three types of traveling modes: walking, bicycling, and driving. We validated the idea based on crime history data from the City of Chicago Portal in December 2012, and sentiments extracted from geotagged tweets during the same time. We discovered that there was a significant correlation between regional Twitter posting sentiments and crime rate, in particular for high-crime and highly negative sentiment areas. We also demonstrated that SocRoutes, by solely utilizing social media sentiments, can find routes that bypass crime hotspots.
- D. M. Austin, L. A. Furr, and M. Spine. The effects of neighborhood conditions on perceptions of safety. Elsevier Journal of Criminal Justice, 2002.Google ScholarCross Ref
- K. Z. Bertrand, M. Bialik, K. Virdee, A. Gros, and Y. Bar-Yam. Sentiment in new york city: A high resolution spatial and temporal view. Tech report arXiv:1305010, 2013.Google Scholar
- P. Goncalves, M. Araujo, F. Benevenuto, and M. Cha. Comparing and combining sentiment analysis methods. In ACM COSN, 2013. Google ScholarDigital Library
- N. Powdthavee. Unhappiness and Crime: Evidence from South Africa. Economica, 2005.Google ScholarCross Ref
- P. Salesses, K. Schechtner, and C. A. Hidalgo. The collaborative image of the city: mapping the inequality of urban perception. PloS One, 2013.Google ScholarCross Ref
Index Terms
- SocRoutes: safe routes based on tweet sentiments
Recommendations
Detecting bursts in sentiment-aware topics from social media
Nowadays plenty of user-generated posts, e.g., sina weibos, are published on the social media. The posts contain the publics sentiments (i.e., positive or negative) towards various topics. Bursty sentiment-aware topics from these posts reveal sentiment-...
Towards building a high-quality microblog-specific Chinese sentiment lexicon
Due to the huge popularity of microblogging services, microblogs have become important sources of customer opinions. Sentiment analysis systems can provide useful knowledge to decision support systems and decision makers by aggregating and summarizing ...
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementAspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are ...
Comments