Event Detection using Deep Learning 
Ariveni Triveni1, Guntur Kesava Sai Adithya2, Sanneboina Karthik3, Deepak Kumar Sahoo4
1A.Triveni*, Department of Computer Science and Engineering , Koneru Lakshmaiah Educational Foundation,Vaddeswaram,Guntur district ,Andhra Pradesh ,India.
2G.Kesavsai, Department of Computer Science and Engineering , Koneru Lakshmaiah Educational Foundation,Vaddeswaram,Guntur district ,Andhra Pradesh ,India.
3S.Karthik, Department of Computer Science and Engineering , Koneru Lakshmaiah Educational Foundation,Vaddeswaram,Guntur district ,Andhra Pradesh ,India.
4Deepak Kumar Sahoo, Assistant Professor, Department of Computer Science and Engineering , Koneru Lakshmaiah Educational Foundation,Vaddeswaram,Guntur district ,Andhra Pradesh ,India.
Manuscript received on April 11, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 243-248 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9426069520/2020©BEIESP | DOI: 10.35940/ijeat.E9426.069520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Event Detection, Long-short term memory (LSTM),Training data Creation, Word Embedding.