Ontology-Based Traffic Accident Information Extraction on Twitter In Indonesia

Traffic accidents become one of the events that often occur in Indonesia. From the three-monthly report by the Indonesian National Police Traffic Police, there are about 25,000 traffic accidents. Many social media users, especially Twitter, share information about traffic accidents. Twitter has various information regarding traffic accidents. Therefore, this study aims to process and map information about traffic accidents contained on Twitter in Indonesia language. We use the domain ontology and Named-Entity Recognition for the data extraction process. Named-Entity Recognition is used for obtaining keywords from a tweet based on class categories such as actor, time, location, and information on the cause of the accident. This research generates a Named Entity Recognition (NER) model that can provide a reasonably accurate level of accuracy. Also, we create an ontology that can categorize the causes of traffic accidents based on the Directorate General of the Land Transportation Office, Indonesia. We found that the traffic accidents are generally caused by inadequate vehicle conditions with the main problem in the vehicle caused by brake failure, while environmental factors rarely cause traffic accidents. Moreover, the vehicle is the subclass that mostly appears in the tweets, where car is the most popular actor, followed by truck and motorcycle.