Abstract:
Microblogging is a very popular way of sharing information about what is happening near realtime.
Microblogs have been used to report about individuals daily life, advertisement or real world
events as they are happening. This data is unstructured, voluminous with constantly changing
topics that needs to be analyzed to make sense out of, in a most effective and efficient way which
can be used during emergency cases. Large scale events like disasters or planned events like a
head of state visiting another country, will normally generate a lot of tweets making it much
simpler to detect. Small scale events like traffic updates, fires, road traffic accidents do not receive
a lot of attention from microblog users hence more difficult to detect.
Firstly reported on microblogs by road users, road traffic accidents are common in our roads which
cause death and traffic congestion if not responded to in a timely manner. In this paper, text mining
through machine learning and NLP techniques was proposed to detect road traffic accidents which
includes, pedestrian, motorcycle, vehicles and other accident incidents on the roads or highways.
In text classification for accident related tweets, SVM was adopted yielding a precision of 90.2%
and a recall of 89.1 %. Location extraction from content proposed method, used syntactic analysis
through Noun phrases and N-gram based matching with integration to local and external location
databases giving a precision of 71.8% and a recall of 83.9%. An application called Traffic Accident
Detection System (TADS) was developed based on the proposed methods where users can locate
accidents in real-time by querying the system, ultimately visualizing the location of the event on
the map for Nairobi, Kenya.