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Finding Data: Digging Deeper into Printed Texts: Home

Supports the Digital Scholars workshop of the same title. How to identify mineable text, sources at Baylor and elsewhere, tools and considerations. Preparations for text mining work with other tools.

Workshop Description

In this workshop you will learn to identify textual data that can be analyzed by various computer applications, learn which resources among the Libraries' databases can be readily used to capture textual data for this purpose, and how to use existing programs and software to enhance and visualize your arguments about connections among ideas as expressed on the printed page across disciplines from historical events, literature, and secondary scholarly literature. No prior experience with text mining, library databases, or computer applications is necessary.

Open to the entire Baylor community.


If you even vaguely think you'd like to be a part of our Data Scholars Program, please fill out the registration form so we and you can keep track of your progress in the program. 

Evaluate this workshop in the Data Scholars Program.

Workshop Materials

Reliable Sources of Texts for Analysis

Social Media Sources

These are BIG data files so not easily accessible on a laptop. Ask for our help.

Video Tutorials

Background Readings

Kobayashi, Vladimer B., Stefan T. Mol, Hannah A. Berkers, Gábor Kismihók, and Deanne N. Den Hartog. “Text Mining in Organizational Research.” Organizational Research Methods 21, no. 3 (July 1, 2018): 733–65.

Usai, Antonio, Marco Pironti, Monika Mital, and Chiraz Aouina Mejri. “Knowledge Discovery out of Text Data: A Systematic Review via Text Mining.” Journal of Knowledge Management 22, no. 7 (May 31, 2018): 1471–88.
Zhou, Shihao, Zhilei Qiao, Qianzhou Du, G. Alan Wang, Weiguo Fan, and Xiangbin Yan. “Measuring Customer Agility from Online Reviews Using Big Data Text Analytics.” Journal of Management Information Systems 35, no. 2 (April 3, 2018): 510–39.

A sampling of recent articles in areas of social work and related disciplines.

Dalianis, H. (2018). Ethics and Privacy of Patient Records for Clinical Text Mining Research. In H. Dalianis (Ed.), Clinical Text Mining: Secondary Use of Electronic Patient Records (pp. 97–108). Cham: Springer International Publishing.
Monsen, K. A., Maganti, S., Giaquinto, R. A., Mathiason, M. A., Bjarnadottir, R. I., & Kreitzer, M. J. (2018). Use of the Omaha System for ontology-based text mining to discover meaning within CaringBridge social media journals. Kontakt.
Park, A., Conway, M., & Chen, A. T. (2018). Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior, 78, 98–112.
Song, I.-Y., Song, M., Timakum, T., Ryu, S.-R., & Lee, H. (2018). The landscape of smart aging: Topics, applications, and agenda. Data & Knowledge Engineering, 115, 68–79.

University Libraries

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