Social Media Data Extraction Method Benchmarking Comparison

Zhenhuan Sui

doi:doi:10.11648/j.ijdst.20190502.12

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Social Media Data Extraction Method Benchmarking Comparison

Zhenhuan Sui

Published in International Journal on Data Science and Technology (Volume 5, Issue 2)

Received: 7 April 2019 Accepted: 13 August 2019 Published: 28 August 2019

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Abstract

Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.

Published in	International Journal on Data Science and Technology (Volume 5, Issue 2)
DOI	10.11648/j.ijdst.20190502.12
Page(s)	40-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Natural Language Processing, Text Analytics, Twitter Analysis, Social Media, Software Analysis, Big Data Analysis

References

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APA Style

Zhenhuan Sui. (2019). Social Media Data Extraction Method Benchmarking Comparison. International Journal on Data Science and Technology, 5(2), 40-44. https://doi.org/10.11648/j.ijdst.20190502.12

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Zhenhuan Sui. Social Media Data Extraction Method Benchmarking Comparison. Int. J. Data Sci. Technol. 2019, 5(2), 40-44. doi: 10.11648/j.ijdst.20190502.12

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AMA Style

Zhenhuan Sui. Social Media Data Extraction Method Benchmarking Comparison. Int J Data Sci Technol. 2019;5(2):40-44. doi: 10.11648/j.ijdst.20190502.12

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@article{10.11648/j.ijdst.20190502.12,
  author = {Zhenhuan Sui},
  title = {Social Media Data Extraction Method Benchmarking Comparison},
  journal = {International Journal on Data Science and Technology},
  volume = {5},
  number = {2},
  pages = {40-44},
  doi = {10.11648/j.ijdst.20190502.12},
  url = {https://doi.org/10.11648/j.ijdst.20190502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190502.12},
  abstract = {Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.},
 year = {2019}
}

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TY  - JOUR
T1  - Social Media Data Extraction Method Benchmarking Comparison
AU  - Zhenhuan Sui
Y1  - 2019/08/28
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdst.20190502.12
DO  - 10.11648/j.ijdst.20190502.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 40
EP  - 44
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20190502.12
AB  - Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.
VL  - 5
IS  - 2
ER  -

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Author Information

Zhenhuan Sui

Department of Integrated Systems Engineering, The Ohio State University, Columbus, USA

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Plain Text BibTeX RIS

APA Style

Zhenhuan Sui. (2019). Social Media Data Extraction Method Benchmarking Comparison. International Journal on Data Science and Technology, 5(2), 40-44. https://doi.org/10.11648/j.ijdst.20190502.12

Copy | Download

ACS Style

Zhenhuan Sui. Social Media Data Extraction Method Benchmarking Comparison. Int. J. Data Sci. Technol. 2019, 5(2), 40-44. doi: 10.11648/j.ijdst.20190502.12

Copy | Download

AMA Style

Zhenhuan Sui. Social Media Data Extraction Method Benchmarking Comparison. Int J Data Sci Technol. 2019;5(2):40-44. doi: 10.11648/j.ijdst.20190502.12

Copy | Download

@article{10.11648/j.ijdst.20190502.12,
  author = {Zhenhuan Sui},
  title = {Social Media Data Extraction Method Benchmarking Comparison},
  journal = {International Journal on Data Science and Technology},
  volume = {5},
  number = {2},
  pages = {40-44},
  doi = {10.11648/j.ijdst.20190502.12},
  url = {https://doi.org/10.11648/j.ijdst.20190502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190502.12},
  abstract = {Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Social Media Data Extraction Method Benchmarking Comparison
AU  - Zhenhuan Sui
Y1  - 2019/08/28
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdst.20190502.12
DO  - 10.11648/j.ijdst.20190502.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 40
EP  - 44
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20190502.12
AB  - Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.
VL  - 5
IS  - 2
ER  -

Copy | Download