Volume 4, Issue 3, May 2019, Page: 49-55
Social Media Text Data Visualization Modeling: A Timely Topic Score Technique
Zhenhuan Sui, Department of Integrated Systems Engineering, The Ohio State University, Columbus, Ohio, USA
Received: Apr. 7, 2019;       Accepted: Jun. 5, 2019;       Published: Jul. 26, 2019
DOI: 10.11648/j.ajmse.20190403.12      View  148      Downloads  21
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.
Keywords
Text Analytics, Natural Language Processing, Cyber Security, Signaling, Pattern Detection, Social Media
To cite this article
Zhenhuan Sui, Social Media Text Data Visualization Modeling: A Timely Topic Score Technique, American Journal of Management Science and Engineering. Vol. 4, No. 3, 2019, pp. 49-55. doi: 10.11648/j.ajmse.20190403.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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