Traffic Detection using Sentimental Analysis

Authors

  • Prof. Mounica B  Professor, Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India.
  • Thejas T R  Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India.
  • Syed Nadeem Pasha  Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India.
  • Swaraj K S  Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India.

DOI:

https://doi.org/10.32628/CSEIT206375

Keywords:

Intelligent Transportation System, CCTV, Piezoelectric sensor, RS232, Magnetic sensor, Convolutional Neural Network

Abstract

Traffic is a major issue in many cities. There are no existing ways which are both cost effective and efficient to measure the traffic. Sentiment analysis is a contextual mining of text, which identifies and extracts subjective information in source material. The Social sites have a huge amount of information because of its vast users. In this project we will use twitter as the source. It is an active site which has many users and also tweets regarding the traffic. Sentiment Analysis is the mechanized procedure of breaking down content information and arranging it into sentiments positive, negative or impartial. Performing Sentiment Analysis on information from Twitter utilizing AI can assist organizations with seeing how individuals are discussing their image. Within excess of 321 million dynamic clients, sending a day by day normal of 500 million Tweets, Twitter permits organizations to contact an expansive crowd and associate with clients without middle people.

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Published

2020-06-30

Issue

Section

Research Articles

How to Cite

[1]
Prof. Mounica B, Thejas T R, Syed Nadeem Pasha, Swaraj K S, " Traffic Detection using Sentimental Analysis" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.354-358, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206375