Social Media Text Data Visualization Modeling: A Timely Topic Score Technique

Zhenhuan Sui

doi:doi:10.11648/j.ajmse.20190403.12

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Social Media Text Data Visualization Modeling: A Timely Topic Score Technique

Zhenhuan Sui

Published in American Journal of Management Science and Engineering (Volume 4, Issue 3)

Received: 7 April 2019 Accepted: 5 June 2019 Published: 26 July 2019

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Abstract

Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.

Published in	American Journal of Management Science and Engineering (Volume 4, Issue 3)
DOI	10.11648/j.ajmse.20190403.12
Page(s)	49-55
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

Text Analytics, Natural Language Processing, Cyber Security, Signaling, Pattern Detection, Social Media

References

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[2]	Allen, T. T., Sui, Z., & Parker, N. L. (2017). Timely decision analysis enabled by efficient social media modeling. Decision Analysis, 14 (4), 250-260. https://doi.org/10.1287/deca.2017.0360.
[3]	Yang, J., & Counts, S. (2010, May). Predicting the speed, scale, and range of information diffusion in Twitter. In Fourth International AAAI Conference on Weblogs and Social Media.
[4]	Shah, D., & Zaman, T. (2010). Community detection in networks: The leader-follower algorithm. stat, 1050, 2.
[5]	Zaman, T., Fox, E. B., & Bradlow, E. T. (2014). A bayesian approach for predicting the popularity of tweets. The Annals of Applied Statistics, 8 (3), 1583-1611.
[6]	Allen, T. T., & Xiong, H. (2012). Pareto charting using multifield freestyle text data applied to Toyota Camry user reviews. Applied Stochastic Models in Business and Industry, 28 (2), 152-163.
[7]	Allen, T. T., Xiong, H., & Afful‐Dadzie, A. (2016). A directed topic model applied to call center improvement. Applied Stochastic Models in Business and Industry, 32 (1), 57-73.
[8]	Allen, T. T., Vinson, S. M., Raqab, A., & Allam, Y. (2013). Using SMERT to Identify Actionable Topics in Student Feedback. Integrated Systems Engineering Technical Report 2013.
[9]	Blei, D. M., Ng, A., & Jordan, M. (2003). Latent Dirichlet allocation Journal of Machine Learning Research (3).
[10]	Allen, T. T., Sui, Z., & Akbari, K. (2018). Exploratory text data analysis for quality hypothesis generation. Quality Engineering, 30 (4), 701-712.
[11]	Feldman, R. and Sanger, J. (2007). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.
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[13]	Teh, Y. W., Newman, D., & Welling, M. (2007). A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. In Advances in neural information processing systems (pp. 1353-1360).
[14]	Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101 (suppl 1), 5228-5235.
[15]	Carpenter, B. (2010). Integrating out multinomial parameters in latent Dirichlet allocation and naive Bayes for collapsed Gibbs sampling. Rapport Technique, 4, 464.

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Zhenhuan Sui. (2019). Social Media Text Data Visualization Modeling: A Timely Topic Score Technique. American Journal of Management Science and Engineering, 4(3), 49-55. https://doi.org/10.11648/j.ajmse.20190403.12

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ACS Style

Zhenhuan Sui. Social Media Text Data Visualization Modeling: A Timely Topic Score Technique. Am. J. Manag. Sci. Eng. 2019, 4(3), 49-55. doi: 10.11648/j.ajmse.20190403.12

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AMA Style

Zhenhuan Sui. Social Media Text Data Visualization Modeling: A Timely Topic Score Technique. Am J Manag Sci Eng. 2019;4(3):49-55. doi: 10.11648/j.ajmse.20190403.12

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@article{10.11648/j.ajmse.20190403.12,
  author = {Zhenhuan Sui},
  title = {Social Media Text Data Visualization Modeling: A Timely Topic Score Technique},
  journal = {American Journal of Management Science and Engineering},
  volume = {4},
  number = {3},
  pages = {49-55},
  doi = {10.11648/j.ajmse.20190403.12},
  url = {https://doi.org/10.11648/j.ajmse.20190403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20190403.12},
  abstract = {Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.},
 year = {2019}
}

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TY  - JOUR
T1  - Social Media Text Data Visualization Modeling: A Timely Topic Score Technique
AU  - Zhenhuan Sui
Y1  - 2019/07/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ajmse.20190403.12
DO  - 10.11648/j.ajmse.20190403.12
T2  - American Journal of Management Science and Engineering
JF  - American Journal of Management Science and Engineering
JO  - American Journal of Management Science and Engineering
SP  - 49
EP  - 55
PB  - Science Publishing Group
SN  - 2575-1379
UR  - https://doi.org/10.11648/j.ajmse.20190403.12
AB  - Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.
VL  - 4
IS  - 3
ER  -

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Author Information

Zhenhuan Sui

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

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Plain Text BibTeX RIS

APA Style

Zhenhuan Sui. (2019). Social Media Text Data Visualization Modeling: A Timely Topic Score Technique. American Journal of Management Science and Engineering, 4(3), 49-55. https://doi.org/10.11648/j.ajmse.20190403.12

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ACS Style

Zhenhuan Sui. Social Media Text Data Visualization Modeling: A Timely Topic Score Technique. Am. J. Manag. Sci. Eng. 2019, 4(3), 49-55. doi: 10.11648/j.ajmse.20190403.12

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AMA Style

Zhenhuan Sui. Social Media Text Data Visualization Modeling: A Timely Topic Score Technique. Am J Manag Sci Eng. 2019;4(3):49-55. doi: 10.11648/j.ajmse.20190403.12

Copy | Download

@article{10.11648/j.ajmse.20190403.12,
  author = {Zhenhuan Sui},
  title = {Social Media Text Data Visualization Modeling: A Timely Topic Score Technique},
  journal = {American Journal of Management Science and Engineering},
  volume = {4},
  number = {3},
  pages = {49-55},
  doi = {10.11648/j.ajmse.20190403.12},
  url = {https://doi.org/10.11648/j.ajmse.20190403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20190403.12},
  abstract = {Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Social Media Text Data Visualization Modeling: A Timely Topic Score Technique
AU  - Zhenhuan Sui
Y1  - 2019/07/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ajmse.20190403.12
DO  - 10.11648/j.ajmse.20190403.12
T2  - American Journal of Management Science and Engineering
JF  - American Journal of Management Science and Engineering
JO  - American Journal of Management Science and Engineering
SP  - 49
EP  - 55
PB  - Science Publishing Group
SN  - 2575-1379
UR  - https://doi.org/10.11648/j.ajmse.20190403.12
AB  - Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.
VL  - 4
IS  - 3
ER  -

Copy | Download