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With as many as 3.5 million passengers using the London underground system every day, it is desirable to examine and understand their interests and opinions, and to harness this information to improve the services of Transport for London (TfL). This research aims to achieve a better understanding of passengers' interests by harvesting text from geo-tagged(More)
This paper explores the data recorded through the Twitter social media service. In particular we are interested in the analysis of the content of Tweet messages. A large corpus of Twitter messages was analyzed and Index of Dissimilarity measure was used to identify interesting words having spatial concentrations. The paper presents an initial exploration of(More)
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