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Text Mining Techniques Implemented to Extract Data from Transit Events in Twitter: A Systematic Literature Review

Abstract:

The use of social networks generates large volumes of data that facilitates the communication and interaction of its users from different approaches and themes. In this context, Twitter is used to post short messages about traffic or transit in cities, becoming a means of direct communication for public and private users. The application of text mining techniques allows extracting relevant information in a specific place and time from this source. Performing a systematic literature review allows to obtain information related to the types of algorithms implemented, the used tools and the results achieved in the application of text mining. The methodology presented by Barbara Kitchenham was applied in order to collect, process, and analyze 456 scientific articles published in digital libraries and bibliographic databases. By using together both research questions and inclusion and exclusion criteria, the research articles were obtained and classified. Also, the assessment of the quality of the systematic literature review was determined based on the number of citations that each article has. The results show the most relevant algorithms, techniques, and tools used in the application of text mining to tweets related to vehicular traffic. This finding allows to expand the line of study both for the monitoring of the network and for the analysis of the messages.