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.
DATA SCHOLARS PROGRAM REGISTRATION:
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
- Zotero GuideThis guide will walk you through how to dowload Zotero and introduce you to using it. This workshop will use features of Zotero to data mine text from the JSTOR electronic resource/database.
- JSTOR: the scholarly journal archive This link opens in a new window
Provides online access to important scholarly journals and books in many different disciplines.
- Using JSTOR for Digital ScholarshipThis guide provides the tools and information for the sample project MLK 50 Digital Scholarship workshop on using Zotero and JSTOR to collect texts for digital analysis.
There is also a companion video tutorial in the Video Tutorial box to the right. - VoyantSimple text mining and analysis tool. Free.
- TextalyserSimple cut and paste
Reliable Sources of Texts for Analysis
- HathiTrust digital library This link opens in a new window
A large-scale collaborative repository of digital content from research libraries including content digitized via Google Books and the Internet Archive digitization initiatives, as well as content digitized locally by libraries.
- Sources: Mineable TextThis guide page lists sources of free text for data mining.
Social Media Sources
These are BIG data files so not easily accessible on a laptop. Ask for our help.
- Internet Archive's Twitter Stream GrabBig data (terabytes worth) not for the general laptop user. Please ask for help from Josh Been, BU Libraries' Digital Scholarship Librarian.
Video Tutorials
Background Readings
- An Introduction to OCR and Searchable PDFsThis guide from the University of Illinois - Urbana/Champaign provides a good intro to OCR, best OCR practices, and information on several OCR software packages. NOTE: Baylor only provides access to Adobe Acrobat PRO (available on campus computers and for annual purchase on personal computers. See https://www.baylor.edu/its/index.php?id=46446
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. https://doi.org/10.1177/1094428117722619.
A sampling of recent articles in areas of social work and related disciplines.
- VADER (Valence Aware Dictionary and sEntiment Reasoner)VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.
- Sentiment analysis and opinion mining [electronic resource] / Bing Liu.Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis.
- Gender bias in sentiment analysis by Mike Thelwall Online Information Review, 02/2018, Volume 42, Issue 1Purpose The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females...
