1. Data and text mining: facilitating our researchers’ needs in the 21st century A case study
Hui Hua Chua
ALCTS CRS College & Research Libraries IG
ALA Annual Conference · Chicago, IL · June 25, 2017
Macro-level presentation > micro-level case study
Speaking about Michigan State University Libraries’ implementation of LexisNexis Web Services Kit for TDM: specifically the decision and process of creating a web-based interface (otherwise known as Text Assembler) to provide campus users unmediated access to search and download news content from the LexisNexis corpus for text mining.

2. What is Text Assembler?
Project context
Implementation process
Lessons learned

3. What is Text assembler? TA user interface Text Assembler WSK API
LexisNexis news corpus
User interface: authenticated users query and view sample search results; queue query for processing and download results
TA: passes query to API; retrieves and stores results for delivery; processes results to create plain text file; manages multiple queries and query processing based on WSK parameters
Not a programmer; not a technical presentation
First 2 components created by MSU Libraries; second 2 licensed from LN and comprise LN WSK

4. Michigan state university
Fall 2016: 50,344 students enrolled
Undergraduate (39,090 or 77.6%) vs graduate (11,254 or 22.4%)
Top five colleges by enrollment: Business (7686), Social Science (6562), Natural Science (6192), Engineering (6075), Agriculture & Natural Resources (4588)
MSU public land grant status and history; in contrast A/L + RCAH = 2115
College Fall 2016/ Headcount/ Rank
Communication Arts and Sciences 3,628 6
Education 3,544 7
Lyman Briggs College 1,968 8
Arts and Letters 1,829 9
Osteopathic Medicine 1,434 10
James Madison College 1,129 11
Nursing 1,116 12
Human Medicine 1,061 13
Veterinary Medicine
MSU College of Law
Music
Residential College in Arts & Humanities

5. TDM landscape in 2014 In flux and developing
Libraries and publishers receiving researcher requests, but often no established business, technical or access models
Ad-hoc access to data requested on case-by-case basis from publisher
License or purchase corpus and host in-house
Project began in 2014

6. MSU Libraries’ role in tdm
Facilitate data access (license negotiation, purchase, data hosting, work with APIs)
Consult or provide assistance with tools, methods or data
Almost always driven by specific researcher request
Only just beginning to think about incorporating TDM language into existing/new licenses.

7. LexisNexis web services kit (wsk)
Product: API plus access to content; enables larger downloads of data than LexisNexis Academic
Content: current news with updates
Business model: subscription
No specific user request or demand
“Let a hundred flowers bloom”
Given this environment, what was different, unique or compelling to MSU about WSK?
Content: Had already licensed or worked with historical news & govt content, journal articles, citation data, Google Books for TDM.
Business model: one-time purchase
Experiment with different products and business models: Let a hundred flowers bloom

8. Implementation team Digital Scholarship Librarian: Thomas Padilla
Digital Library Programmer: Devin Higgins
Programmer: Megan Schanz
Liaison to School of Journalism: Hui Hua Chua

9. Implementation timeline & process
7/2014
WSK acquired
8/ /2014
Fact-finding & testing
In-house  
LN sales and technical staff 
Other universities that had licensed WSK
Potential MSU users and use cases
Decision made to develop unmediated web-based user interface. Why? Technical considerations and large potential user base
Read documentation; use test API; internal discussions.
LN sales and technical staff: content, license and technical use restrictions, test API, documentation
Temple University: how WSK is used and how access is managed; resources required
MSU poli sci faculty: text-mining needs and use cases for current news/legal content, specific current research question.
Decision made to develop unmediated web-based user interface based on technical considerations and potential large user base

10. Implementation timeline & process
11/2014-1/2015
Direct API use to answer 2 specific research questions
12/2014
Project request submitted to MSUL Systems department
2/2015
Programmer assigned. System developed (1.5 months)
8/2015
Usability testing of UI
9/2015
Public launch of Text Assembler
12/2016
Permission received from MSU Technologies to share code with acknowledgement. Source code:
One major software update since launch. 
The ongoing maintenance time is very low, the same amount of time spent maintaining all of our servers and nothing specific to Text Assembler. Maybe a few hours per month total.

13. Lessons learnt: before implementation
Better coordination of acquisition and implementation processes. Fact-finding could have been completed before purchase, leading to less time to public launch.
Somewhat limited as documentation only available after purchase. But would have saved some time (1 year to launch)

14. Lessons learnt: post implementation
How to balance system constraints with user behavior or preferences
System update (1 week development time)
Work with users to formulate more targeted queries
Hourly query rotation
More guidance needed with query formulation
More explicit search instructions, specifically for selecting appropriate data sources and field searching
Work with users individually
Takes a while for new system to be adopted and used
Without specific use cases we were learning about users as they used the system, so lessons learnt were mainly about our users.
10-page Boolean query
How many of you here are familiar with LN Academic?
LN Academic data structure not easy to understand
Oct first query queued.

15. Usage Queued searches since system went live 121
Number of different users in the last 3 months 5
Average number of results per search
24,855
Largest search
299,146
Usage statistics and privacy

16. Usage continued Based on review of specific queries and user contacts
Primarily used as research tool; not the UG teaching tool we had hoped for
Heaviest use by/in the Social Sciences
Library relationship with TA users is arms-length
Similar to any third-party database. Hear from users when have problems.
- Contrast to other libraries such as Vanderbilt that have adopted a different approach: collaboration with users on research projects, skills and community building.
This is OK if receiving heavy use but isn’t.

17. Further questions How is success defined?
How do libraries define, assess and demonstrate value in TDM services?
Potential assessment of
contribution to research outputs
use in teaching
Questions I have that we can maybe discuss in Q&A later

18. Thank you!
Hui Hua Chua Collections & User Support Librarian Michigan State University Libraries TA source code:
Technical questions? I can pass them on to the appropriate person.