Twitter Information Extraction for Smart City

In Indonesia, Bandung is the second most active Twitter user, which means a lot of tweets have been shared among Bandung people on Twitter. Tweets can be used as a data source to explore information related to the city. One example is information related to traffic congestion, such as information of...

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Bibliographic Details
Published in2014 International Conference on ICT For Smart Society (ICISS) pp. 295 - 299
Main Authors Hanifah, Raidah, Supangkat, Suhono Harso, Purwarianti, Ayu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2014
Subjects
Accuracy
Cities and towns
Data mining
Feature extraction
information extraction
social sensor
Support Vector Machine (SVM)
traffic congestion
Twitter
Uniform resource locators
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Summary:In Indonesia, Bandung is the second most active Twitter user, which means a lot of tweets have been shared among Bandung people on Twitter. Tweets can be used as a data source to explore information related to the city. One example is information related to traffic congestion, such as information of location, date, and time when the traffic congestion happened. In this study, we proposed a method to filter the tweets related to traffic congestion in Bandung and to extract the information of location, time, date and image (if any). SVM with several variations of the weighting and the selection of features is used for filtering process. The results showed that the greatest accuracy rate is 83% use Binary weighting method in top-2000 features. Meanwhile, information extraction process carried out by a rule-based approach, gave satisfactory results, around 98%-100% for the extraction of date, time and URL. However, the extraction of location information only gave accuracy of about 62%. It was caused by OOV (Out of Vocabulary) and OOR (Out of Rules).
DOI:10.1109/ICTSS.2014.7013190