Design and Analysis of a Tweet Alert System for Identifying Real Time Traffic Using K-Means Clustering Algorith
D D D Suribabu1, T Hitendra Sarma2, B Eswar Reddy3
1D D D Suribabu, Research Scholar, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Ananthapur, Andhra Pradesh, India.
2Dr.T Hitendra Sarma, Professor, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Ananthapur, Andhra Pradesh, India.
3Dr. B Eswar Reddy, Professor & Principal, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Ananthapur, Andhra Pradesh, India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 151-156 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0031028419/2019©BEIESP
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© 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: At present, one of the major issues for an individual to meet their requirements is the disordered traffic. In order to resolve this issue, this proposed thesis focuses on designing an application in order to classify each and every individual tweet based on the traffic related keywords and assign a unique label for all tweets. If any message, which contains traffic-related information, it is being sent as an alert to the end users who are following the current user, or else the same tweet will be just posted on the user wall. In the digital era, the social networks have become a fascinating domain for every human for sharing and communicating their recent updates among each other. In order to implement this application, it chooses a compatible social media that is Twitter, for sending traffic related tweets to the followed users. This problem is solved by applying the K-Means algorithm for identifying the traffic-related keywords from the tweet and then clustering the traffic tweets and normal tweets into two separate categories.
Keywords: Traffic Tweets, Tweet Classification, Social Networks, Text Mining Technique, Twitter Stream Analysis, K_Mean.
Scope of the Article: Classification