Anomaly Detection In Practice Using Python

Authors

  • Bari S Department of Computer Science, Alard College Engineering and Management, Pune, India
  • Patankar A Department of Information Technology, D.Y. Patil College of Engineering Aakurdi, Pune, India

DOI:

https://doi.org/10.26438/ijcse/v7i7.241246

Keywords:

Anomaly, Types of Anomalies, Machine Learning, Text Stream, Twitter Data, Social media analysis

Abstract

On 8th August 2018, Kerala had a very heavy rainfall, resulting filling of dams caused flood situation in Kerala. Many people started posting twits about this and people living in that area were alerted. Administration department started their rescue operations. Here social media played key role in locating people and providing help to them. A lot of campaigns were started to collect financial aid to the affected people. Here we again felt power of social media that can positively impact the society. Twitter, a popular micro blogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. It is also extremely popular because the information gets spread more widely and rapidly. It’s important to detect anomalous events which are trending on the social media and be able to monitor their evolution and find related events. This paper talks about how to detect the anomalies in tweets.

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Published

2019-07-31
CITATION
DOI: 10.26438/ijcse/v7i7.241246
Published: 2019-07-31

How to Cite

[1]
S. Bari and A. Patankar, “Anomaly Detection In Practice Using Python”, Int. J. Comp. Sci. Eng., vol. 7, no. 7, pp. 241–246, Jul. 2019.

Issue

Vol. 7 No. 7 (2019): IJCSE July Edition

Section

Survey Article

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