Mining Application on Analyzing Users’ Interests from Twitter

Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on March 26-27, 2018

8 Pages Posted: 3 May 2018

See all articles by Arti Jain

Arti Jain

Jaypee Institute of Information Technology (JIIT)

Ashutosh Gupta

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

Nikhil Sharma

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

Shubham Joshi

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

Divakar Yadav

Madan Mohan Malaviya University of Technology - Department of Computer Science and Engineering

Date Written: April 20, 2018

Abstract

In today’s world, it is problematic to provide users of social-media with posts that are analyzed from their interest efficiently. Users are unable to see the good quality and variety of posts based on their interest. The mass adoption of smartphones along with an internet connection via wi-fi or cellular network enables to analyse users’ interest from Twitter. Twitter is used by a large number of audience to share their posts on a variety of topics as tweets. Then mining users’ interests from Twitter can amplify a number of efficacies, such as advertising, trending topics that can be analyzed by interests and recommendation of users’ posts. For this purpose, this paper provides an Android application which incorporates Web Services, Jsoup, JSON, Firebase Real-time Database and MVC. The application aids to select the posts which include spectacular images and text that are shown to users as a training set. The personalized posts can later be inferred and analyzed by the users themselves using Suffix Array Data Structure and Artificial Neural Network (ANN). Under ANN, we have used Backpropagation methodology that fires neurons as posts. Kosaraju algorithm and Palette library then help in removing redundant posts while later one also retaining relevant posts with specified hashtags more efficiently and accurately.

Keywords: Android, Backpropagation, Firebase, Kosaraju, Neural Networks, Suffix Array, Twitter

Suggested Citation

Jain, Arti and Gupta, Ashutosh and Sharma, Nikhil and Joshi, Shubham and Yadav, Divakar, Mining Application on Analyzing Users’ Interests from Twitter (April 20, 2018). Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on March 26-27, 2018, Available at SSRN: https://ssrn.com/abstract=3166015

Arti Jain (Contact Author)

Jaypee Institute of Information Technology (JIIT) ( email )

A-10
Sector-62
Noida, Uttar Pradesh 201307
India

Ashutosh Gupta

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

A-10
Sector-62
Noida, Uttar Pradesh 201307
India

Nikhil Sharma

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

A-10
Sector-62
Noida, Uttar Pradesh 201307
India

Shubham Joshi

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

A-10
Sector-62
Noida, Uttar Pradesh 201307
India

Divakar Yadav

Madan Mohan Malaviya University of Technology - Department of Computer Science and Engineering

Deoria Road, Singhariya, Kunraghat
Gorakhpur, Uttar Pradesh 273016
India

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