Mining social media data

In recent years, there have been a huge growth in the use of social media. Despite the huge amount of social media data available, they are still not fully utilised. Hence, there is a need for social media mining to find patterns and make sense of the data available. This study sought to predi...

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Main Author: Teo, Kelvin Mo Sheng
Other Authors: Ke Yiping, Kelly
Format: Final Year Project
Language:English
Published: 2016
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Online Access:http://hdl.handle.net/10356/66709
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-667092023-03-03T20:38:45Z Mining social media data Teo, Kelvin Mo Sheng Ke Yiping, Kelly School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications In recent years, there have been a huge growth in the use of social media. Despite the huge amount of social media data available, they are still not fully utilised. Hence, there is a need for social media mining to find patterns and make sense of the data available. This study sought to predict popular topics by examining them on Twitter over a time-window of 7 days. Through the application of three classification algorithms, namely, Decision Tree Classifiers, Naïve Bayes Classifiers and Support Vector Machines, and compare the performance of these three classification algorithms to find the most effective algorithm for mining two different types of class labels, Absolute and Relative Addressing. The results obtained showed that Support Vector Machines produced more accurate results while taking a substantial amount of time to process. Decision Tree Classifiers, on the other hand, took a much shorter time to process, but still able to predict with only a slightly lower accuracy than Support Vector Machines. Therefore, mining Twitter data prove to be useful in predicting popular topics, and mining social media data can be an effective method for commercial purposes. While this study focuses only on three classification algorithms and one data set with two types of class labels, further studies on other social media, algorithms and more data sets can be done in order to provide more accurate and comprehensive findings. Bachelor of Engineering (Computer Science) 2016-04-21T08:14:29Z 2016-04-21T08:14:29Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66709 en Nanyang Technological University 38 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
spellingShingle DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
Teo, Kelvin Mo Sheng
Mining social media data
description In recent years, there have been a huge growth in the use of social media. Despite the huge amount of social media data available, they are still not fully utilised. Hence, there is a need for social media mining to find patterns and make sense of the data available. This study sought to predict popular topics by examining them on Twitter over a time-window of 7 days. Through the application of three classification algorithms, namely, Decision Tree Classifiers, Naïve Bayes Classifiers and Support Vector Machines, and compare the performance of these three classification algorithms to find the most effective algorithm for mining two different types of class labels, Absolute and Relative Addressing. The results obtained showed that Support Vector Machines produced more accurate results while taking a substantial amount of time to process. Decision Tree Classifiers, on the other hand, took a much shorter time to process, but still able to predict with only a slightly lower accuracy than Support Vector Machines. Therefore, mining Twitter data prove to be useful in predicting popular topics, and mining social media data can be an effective method for commercial purposes. While this study focuses only on three classification algorithms and one data set with two types of class labels, further studies on other social media, algorithms and more data sets can be done in order to provide more accurate and comprehensive findings.
author2 Ke Yiping, Kelly
author_facet Ke Yiping, Kelly
Teo, Kelvin Mo Sheng
format Final Year Project
author Teo, Kelvin Mo Sheng
author_sort Teo, Kelvin Mo Sheng
title Mining social media data
title_short Mining social media data
title_full Mining social media data
title_fullStr Mining social media data
title_full_unstemmed Mining social media data
title_sort mining social media data
publishDate 2016
url http://hdl.handle.net/10356/66709
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