TY  - JOUR
TI  - Real-Time Event Recognition and Earthquake Reporting System Development by Using Tweet Analysis
AU  - M. Vijay Kumar
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2018/04/30
PY  - 2018
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT1833415
VL  - 3
IS  - 4
SP  - 459	
EP  - 464
AB  - Twitter has received a lot of attention recently. A very important characteristic of Twitter is its period of time nature. Abstraction event foretelling from social media is probably very helpful however suffers from essential challenges, like the dynamic patterns of options (keywords) and geographic non uniformity (e.g., abstraction correlations, unbalanced samples, and totally different populations in numerous locations). Most existing approaches (e.g., LASSO regression, dynamic question enlargement, and burst detection) address some, however not all, of those challenges. We tend to investigate the period of time interaction of events like earthquakes in Twitter Associate in Nursing propose a rule to observe tweets and to observe a target event. To observe a target event, we tend to devise a classifier of tweets supported options like the keywords during a tweet, the quantity of words, and their context. Later, we tend to turn out a probabilistic spatiotemporal model for the target event which will realize the middle of the event location. We tend to regard every Twitter user as a sensing element and apply particle filtering, that area unit wide used for location estimation. The particle filter works higher than different comparable strategies for estimating the locations of target events.