2. What is Text Mining? There is no single definition of text mining. In general, text mining is a subdomain of data mining that primarily deals with

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1 1. Title Slide 1

2 2. What is Text Mining? There is no single definition of text mining. In general, text mining is a subdomain of data mining that primarily deals with textual documents rather than discrete data. Text mining gathers documents into corpora and then applies statistical techniques to find associations or patterns between terms. There is no single way of going about mining texts. The methods and technologies used will be dictated by the research goals and researchers skills. 2

3 3. Types of Projects Automated, machine enabled text mining has been employed for years across the academe. In business and its sub disciplines, there have been frequent applications of the method; Researchers have looked at sources such as published news articles to measure economic uncertainty, media's role in the stock market based on central bank communications. Corporate disclosures, 10ks are a deep well recent research using 10ks, has measured firm financial constraints and have been used as a predictor for detecting firm fraud. Online product reviews, are a gold mine for textual analysis about consumers and brands, and product choice. Social media is yet another textual treasure, researchers are looking at sentiment on Twitter as a market predictor. Before I talk about our project, I ll give you a little context. 3

4 4. Case Study: Institutional Context Vanderbilt is a ranked liberal arts university with an attached Medical Center in the center of a rapidly growing Southeastern city. The University has just under 12,000 fulltime graduate and undergraduate students across ten schools and multiple disciplines. Vanderbilt s Strategic Plan, drives university initiatives. The Plan is predicated on a number of goals and four pillars. Two of which are relevant to this project: Trans Institutional Programs cross disciplinary research and study. Education Technologies, which we call EdTech Because the project is so clearly attached to the universities goals, it made it easier to take on. In addition to the universities strategic plan, the libraries at Vanderbilt have embraced the liaison model trend, which includes the concept of functional specialists AND liaisons where liaisons are experts in their areas, may possess an additional functional role, but also rely on centralized functional specialists to be a more flexible workforce. And you will see, that is certainly case here. 4

5 5. Case Study: Owen & Management Library The Walker Management Library is physically located, in the Owen Graduate School of Management, which offers graduate level business programs. We are a relatively young and small program, being a liberal arts university, there is no undergraduate business major on campus. Across all business degree programs there are just under 600 students. The Management library has the equivalent of three full time liaisons. 5

6 6. Case Study at Management This case study starts with a simple reference transaction. This particular question is a relatively common question, I m sure all of you have received it. This time was different, we held an ongoing reference interview, that in some ways is still going on. 6

7 7. A group of faculty researchers, all broadly interested in the area of financial reporting, earnings forecasts, and external influences on financial statements, began to explore the quarterly earnings calls between a firm's management and investment analysts. The researchers are particularly interested in the sentiment of the Question and Answer (Q&A) portion of the quarterly calls. Specifically, they are interested in the tone, and the possibility of underlying communication understood between firm and analyst, but veiled to the common investor. To understand these interactions further, the researchers set out to conduct sentiment analysis on a large set of conference calls. They were looking for about 15 years of transcripts for about 500 firms. That is 30,000 transcripts. 7

8 8. Due to The US s Fair Disclosure regulation, quarterly earnings calls are made available to the investing public. With their liaison, we set out to identify the best source for this research project. Although many of the transcripts are publicly available, ensuring a "complete" set was not a given. By "complete," I mean all quarters for all the firms in all the period years. This is not necessarily easily done through web scraping of sites like Seeking Alpha, which provide many, but not necessarily all, earnings call transcripts on the open web. Another option was to look at proprietary products already licensed, like these usual suspects. However, it became clear that to build the corpus with necessary breadth and depth, with tens of thousands of transcripts, licensing issues around mass downloading would arise. In both the open web and library product options, text format was also an issue. To conduct the desired sentiment analysis, the transcripts ultimately needed to be in machine readable format. We began to look at license addendums to current products and additional sources to see who could provide the mass of transcripts in a format conducive to computational analysis. After evaluation and consideration of available resources, different licensing options, technical requirements, costs, and customer services, the librarian and researchers honed in on a product that would best meet the research need. 8

9 8

10 9. The LexisNexis WSK (Web Services Kit) provides an API to the same content already licensed in the LexisNexis Academic product including FDWire earnings call transcripts from 2001 to present day. The WSK allows researchers to bypass the web interface, create custom searches and mass download documents into HTML format. An addendum a set up fee and a subscription fee are required to add the WSK to an existing LexisNexis Academic license. In a partnership between the Owen Graduate School of Management and the Vanderbilt Library, the WSK product was licensed. The licensing is managed by the library, the initial costs shared by both entities. because this product has applications across our campus it was an easier sell to our library administration.. With the strategic plan so clear, in that we were to engage in ED Tech and cross campus research, we were able to make this new type of acquisition. 9

11 10. Early in the source analysis process, it became clear that the library had far more to offer this research project than identification, evaluation, and licensing of information. Our library system has functional librarians, one with expertise in APIs, XML modeling and sentiment analysis, which would be helpful to the project. Our director of scholarly communications and our data curation postdoctoral fellow were called in to consult. Consultation grew into training as well as ongoing collaboration, involving the development of the API, the extraction of data using XQuery as well as work on dictionary tools and the sentiment analysis. The partnership between the library and the business school researchers now includes both technical expertise as well product licensing and access. This partnership was solidified with a Memorandum of Understanding (MOU). The MOU defined the scope of the project and library involvement, in terms of time and money. It also included a mutual agreement that those involved would communicate widely about the availability of the newly licensed materials. 10

12 11. Although the Owen researchers were satisfied to have the LexisNexis API for their slice of earnings call transcripts, the broad range and depth of resources available in the database meant that many disciplines across Vanderbilt's campus would potentially have interest in the tool. So, we shared broadly. The communication spawned a number of additional text mining projects across the campus. The earnings calls project is ongoing and has not reached publication phase. 11

13 12. Stages of Research The regular stages of research apply to text mining projects too. In this case study, we have touched on parts throughout the whole research process. One thing that I d like to talk more about and talk with others about are licensing issues issues that might arise when text mining with our current academic licenses Systematically downloading or distributing large volumes of material. Sharing content with unauthorized users, for format conversion or analysis Some believe explicitly adding text mining language to academic licenses then assumes that the practice becomes and exception not the implied rule another avenue for vendors to charge. Considering licensing for text mining a couple things to consider: include or not include. Should add Curation and Preservation to this research model. 12

14 13. Scaling the Model Scaling a new service like text mining can be challenging, especially with current library budgets. As interest in text mining increases, libraries are in a unique position to enable access and develop skills to support researchers utilizing the method. These are some thoughts on ways to scale up text mining services. creating curated collections and lists of resources available for text mining; preparing a list of considerations for researchers thinking about starting a text mining project; writing how to documents for getting started with text mining technical tips and considerations; providing opportunities for researchers to gain experience with text mining, including the data lifecycle: extraction, analysis and storage; constructing tools for the research community to self serve text mining resources; developing platforms, like GitHub, for researchers and librarians to share code and best practices; and increasing the technical skills and text mining know how of librarians and others at their business schools. Scaling up support for text mining initiatives will require expanding liaison skill sets and 13

15 partnering with functional experts at the university, hopefully from the library, but possibly IT. 13

16 14. Developing New Skills Researchers' lack of technological skills and knowledge of coding, developing, or mining practices is an opportunity for libraries. Librarians have opportunities to lower the technological barriers to text mining by offering functional skill sets, providing occasions for skill development, perhaps developing self help tools. The majority of librarians have not yet been trained in these areas, but we can partner with informational technologists elsewhere at the institution, or train librarians to develop these new skills. At Vanderbilt we are doing this with workshops and working groups. 14

17 15. Conclusion Leveraging the subject expertise of liaison librarians and developing our functional expertise allowed us to partner with researchers on this text mining project. I hope that some of the things we have learned along the way will be helpful to others as you develop scalable models for your institutions. I and our director of scholarly communications have submitted a paper on this project. 15

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