Open AccessEvent Detection using Deep Learning
Author(s) -
A Triveni,
G Adithya,
S. Karthik,
Deepak Kumar Sahoo
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9426.069520
Subject(s) - computer science , event (particle physics) , context (archaeology) , task (project management) , social media , point (geometry) , filter (signal processing) , word embedding , embedding , word (group theory) , artificial intelligence , world wide web , engineering , computer vision , geography , linguistics , philosophy , physics , geometry , mathematics , archaeology , systems engineering , quantum mechanics
Now a days, Twitter posts more than 400 million tweets every day can disclose real-world details as events grow. Event detection is a method to find real events which occur over time and space. Recent social media networks, such as Face Book, Instagram, Whatsapp and Twitter have been widely documented in real time. In the case of an earthquake, for example, people report earthquake-related information instantly, which allows the earthquake to be quickly detected. In this paper, we have developed a data filter based on functions like keywords, numbers and context. Every user feed is viewed as a sensor and such sensor selection provides a device capable of alerting registered users immediately. Using word embedding models, tweets are converted into numerical vectors. Tweets are classified into political, criminal, social, medical, disaster and miscellaneous predefined classes. Classification task is done by using long short-term memory networks (LSTM). A large number of tweets for the creation and testing of our proposed model are obtained via the Twitter API endpoint, which is marked as an effective technique