Tweet
Analysis for Real-Time Event Detection and Earthquake Reporting System
Development
ABSTRACT:
Twitter has received much attention
recently. An important characteristic of Twitter is its real-time nature. We
investigate the real-time interaction of events such as earthquakes in Twitter
and propose an algorithm to monitor tweets and to detect a target event. To
detect a target event, we devise a classifier of tweets based on features such
as the keywords in a tweet, the number of words, and their context.
Subsequently, we produce a probabilistic spatiotemporal model for the target
event that can find the center of the event location. We regard each Twitter
user as a sensor and apply particle filtering, which are widely used for
location estimation. The particle filter works better than other comparable
methods for estimating the locations of target events. As an application, we develop
an earthquake reporting system for use in Japan. Because of the numerous
earthquakes and the large number of Twitter users throughout the country, we
can detect an earthquake with high probability (93 percent of earthquakes of
Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are
detected) merely by monitoring tweets. Our system detects earthquakes promptly and
notification is delivered much faster than JMA broadcast announcements.
EXISTING SYSTEM:
Twitter is categorized as a
microblogging service. Microblogging is a form of blogging that enables users
to send brief text updates or micro media such as photographs or audio clips.
Microblogging services other than Twitter include Tumblr, Plurk, Jaiku,
identi.ca, and others. Users can know how other users are doing and often what
they are thinking about now, users repeatedly return to the site and check to
see what other people are doing
DISADVANTAGES
OF EXISTING SYSTEM:
1.
Each Twitter user is regarded as a
sensor and each tweet as sensory information. These virtual sensors, which we
designate as social sensors, are of a huge variety and have various characteristics:
some sensors are very active and others are not.
2.
A sensor might be inoperable or
malfunctioning sometimes, as when a user is sleeping, or busy doing something
else.
3.
Social sensors are very noisy compared
to ordinary physical sensors. Regarding each Twitter user as a sensor, the
event-detection problem can be reduced to one of object detection and location
estimation in a ubiquitous/ pervasive computing environment in which we have
numerous location sensors: a user has a mobile device or an active badge in an
environment where sensors are placed.
PROPOSED
SYSTEM:
This paper presents an investigation of
the real-time nature of Twitter that is designed to ascertain whether we can
extract valid information from it. We propose an event notification system that
monitors tweets and delivers notification promptly using knowledge from the
investigation. In this research, we take three steps: first, we crawl numerous
tweets related to target events; second, we propose probabilistic models to extract
events from those tweets and estimate locations of events; finally, we
developed an earthquake reporting system that extracts earthquakes from Twitter
and sends a message to registered users.
ADVANTAGES
OF PROPOSED SYSTEM:
The advantages of this paper are
summarized as follows:
ü The
paper provides an example of integration of semantic analysis and real-time
nature of Twitter, and presents potential uses for Twitter data.
ü For
earthquake prediction and early warning, many studies have been made in the
seismology field. This paper presents an innovative social approach that has
not been reported before in the literature.
ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA
ü Java Version :
JDK 1.6 & above.
REFERENCE:
Takeshi Sakaki, Makoto Okazaki, and
Yutaka Matsuo Tweet Analysis for Real-Time Event Detection and Earthquake
Reporting System Development, IEEE
TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 4, APRIL 2013
