Tag Archives: TextMining

DH Box: Tackling Project Scope

We have this great Digital Humanities project idea, but what happens between now and launch time?

With an idea like DH Box (a customized linux OS with preinstalled DH Tools and the flexibility to operate on a computer as cheap and portable as the Raspberry Pi) there are a number of directions we could take, and will certainly consider for further iterations of DH Box beyond the Spring term (this blog currently documents the experiences of a project team enrolled in a graduate course in Digital Humanities Praxis at the Graduate Center, CUNY).

In order to refine the scope of our tool, we asked ourselves some questions:

  • What approach will we take around educating users about coding, the infrastructure around the DH Box software, hardware, and operating system?
  • Which DH Tools should we include? See Alan Liu’s curated list for more info on the scope of DH tools out there
  • What user(s) are we building this for?

The success of our project hinges on our ability to carefully model the scope of the tool by shaping the answers to these questions . . . all by May 12th (public launch date)!

Educational Value

Beyond providing a collection of accessible DH Tools, we want DH Box to help bridge knowledge gaps by delivering a strong educational component. We’d like for instance, undergraduate English students to gain exposure and develop proficiency in Digital Humanities inquiry through the kind of guidance and practical experience DH Box will offer. To that end, we will begin an interactive textbook to provide instruction about the specific tools included in this first iteration of DH Box. We are most inspired by the Learn Code the Hard Way interactive textbook series by Zed Shaw.


We are gearing this version of DH Box to bring Topic Modeling and Text Analysis to Humanities students!

We began by considering the most popular DH Tools out there and quickly realized it made a lot of sense to whittle the list down for this current project phase. We’ve made choices based on optimal software performance with the Raspberry Pi. We also want to provide DH Tools that haven’t yet had the level of proliferation like some of the more popular content management systems such as WordPress.


Undergraduate Humanities students currently have little familiarity with terms like tokenizationsentiment analysis, etc., and how these components of text analysis can open expansive modes of textual inquiry. As part of its mission, DH Box will work to make these methods accessible to a broad audience!

Stay tuned for exciting updates on implementing the install scripts, using IPython Notebook, and more!


Questions? Comments? Tweet us!

Easy Access to Data for Text Mining

Prospect Workflow

Will 2014 be the year that you take a huge volume of texts and run them through an algorithm to detect their themes? Because significant hurdles to humanists’ ability to analyze large volumes of text have been or are being overcome, this might very well be the year that text mining takes off in the digital humanities. The ruling in the Google Books federal lawsuit that text mining is fair use has removed many concerns about copyright that had been an almost insurmountable barrier to obtaining data. Another sticking point has been the question of where to get the data. Until recently, unless researchers digitized the documents themselves, the options for humanities scholars were mostly JSTOR’s Data for Research, Wikipedia and pre-1923 texts from Google Books and HathiTrust. If you had other ideas, you were out of luck. But within the next few months there will be a broader array of full-text data available from subscription and open access databases.

CrossRef, the organization that manages Digital Object Identifiers (DOIs) for database publishers, has a pilot text mining program, Prospect, that has been in beta since July 2013 and will launch early this year. There is no fee for researchers who already have subscription access to the databases. To use the system, researchers with ORCID identifiers log in to Prospect and receive an API token (alphanumeric string). For access to subscription databases, Prospect displays publishers’ licenses that researchers can sign with a click. After agreeing to the terms, they receive a full-text link. The publisher’s API verifies the token, license, and subscription access and returns full-text data subject to rate limiting (e.g. 1500 requests per hour).

Herbert Van de Sompel and Martin Klein, information scientists who participated in the Prospect pilot, say “The API is really straightforward and based on common technical approaches; it can be easily integrated in a broader workflow. In our case, we have a work bench that monitors newly published papers, obtains their XML version via the API, extracts all HTTP URIs, and then crawls and archives the referenced content.”

The advantage for publishers is that providing access to an API may stop people from web scraping the same URLs that others are using to gain access to individual documents. And publishers won’t have to negotiate permissions with many individual researchers. Although a 2011 study found that when publishers are approached by scholars with requests for large amounts of data to mine they are inclined to agree, it remains to be seen how many publishers will sign up for the optional service and what the license terms will be. Interestingly, the oft-maligned Elsevier is leading the pack having made its API accessible to researchers during the pilot phase. Springer, Wiley, Highwire and the American Physical Society are also involved.

Details about accessing the API are on the pilot support site and in this video. CrossRef contacts are Kirsty Meddings, product manager [kmeddings@crossref.org] and Geoffrey Bilder, Director of Strategic Initiatives [gbilder@crossref.org].


The Twenty-First Century Footnote, Part Two

In Part One of this blog post, I wrote about scholars’ reliance on proprietary databases for research and the importance of understanding the constraints which database structures place on the outcomes of their efforts. Unfortunately, generally speaking, information about the structures of proprietary databases is not easily accessible. To remedy this, Caleb McDaniel has talked about the need to create an online resource to collate information about the construction of proprietary databases.

As an exploration of the structure of a proprietary database, I will look at one commercial database’s search and text analysis tools and touch on their handling of content. My goal is to demonstrate some of the complexity of these systems and to parse out the types of information that scholars would want to know and should consider sharing when writing up their research findings.

Artemis – Text mining lite

I recently attended a presentation about a commercial database company’s venture into what I call “text mining lite.” The company, Gale, has just started to offer text analysis and other tools that are squarely aimed at the field/set of methods of digital humanities. The tools are available through Artemis, an interface that allows searches across multiple collections of primary eighteenth century (ECCO) and nineteenth century sources (NCCO). There is a separate Artemis platform for literary material with the same analytic features. By 2015 Gale humanities collections running the gamut from the 19th Century U.S. Newspapers to the Declassified Documents Reference System and many others will migrate into Artemis. Artemis is available CUNY-wide.

Parameters of search

To access Artemis’s textual analysis capabilities the user first determines the parameters of selection of the materials. The options are extensive: date ranges, content type (e.g. manuscript, map, photograph), document type (e.g. manifesto, telegram, back matter), title, and source library. For example, one could search only letters from the Smith College archives or manuscripts from the Library of Congress in particular years.


Discussing the use of Google’s Ngram to find themes in large bodies of texts, Matt Jockers advises caution, “When it comes to drawing semantic meaning from a word, we require more than a count of that word’s occurrence in the corpus. A word’s meaning is derived through context” (120). In his CUNY DHI and Digital Praxis Seminar lecture, David Mimno addressed the necessity of understanding the context of words in large corpora saying, “We simply cannot trust that those words that we are counting mean what we think they mean. That’s the fundamental problem.”

One way that Artemis deals with this is by offering a view into the context of the documents in search results. For each result, clicking on “Keywords in Context” brings up a window showing the words surrounding the keyword in the actual (digital facsimile) document. This makes it relatively simple to identify if the document is actually relevant to your research, as long as the number of documents being examined is not too large.

Refining results

While the categories of search that Artemis allows are quite flexible, it is also possible to enter proximity operators to find co-located words. This means that, in many situations, it will be possible to further refine results through iterative searching to locate smaller batches of relevant documents on which to run the text analysis tools.

Ngram viewer

Artemis features a visualization tool that offers some improvements over Google’s Ngram to show frequency of terms over time. The term frequency ngram is created from the search results. Click and drag on the term frequency graph to modify the date range. The graph can zoom to the one-year level. It is possible to retrieve a particular document by clicking on the point on the graph. The visualization also displays term popularity, the percent of the total documents each year. Term popularity normalizes the number of documents based on the percentage of the content.

Term clusters visualization

For larger sets of documents, or to look at entire collections, researchers might want to use term clusters. Term clusters use algorithms to group words and phrases that occur a statistically relevant number of times within the search results.

The visualization of term clusters are based on the first 100 words of the first 100 search results per content type. This means that the algorithm would run only within, for example, the first one hundred words of the first one hundred monographs, the first one hundred words of the first one hundred manuscripts, and the first one hundred words of the first one hundred newspaper articles. The size limitations are because the text analysis tools are bandwidth intensive. Searches of larger numbers of documents take longer to return results and also slow down the system for other users. By clicking on the clusters, it is possible to drill down into the search results to the level of individual documents and their metadata.

Legibility of documents

Scholars should have an understanding of the process by which database publishers have transformed documents into digital objects because it affects the accuracy of searches and text analysis. In Gales’ collections, printed materials are OCR’d. For nonprint materials, such as manuscripts, ephemera and photograph captions, the metadata of names, places and dates are entered by hand. By providing improved metadata for nonprint materials, Gale has increased the discoverability of these types of documents. This is particularly important for those studying women and marginalized groups whose records are more likely to be found in ephemeral materials.

Collection descriptions

Understanding the types of materials contained within a proprietary database can be difficult. The Eighteenth Century Collections Online (ECCO) is based on the English Short Title Catalogue from the British Library and is familiar to many scholars of the eighteenth century. The Nineteenth Century Collections Online (NCCO) is a newer grouping of collections that is being continually updated. To see a detailed description of the collections in NCCO, go to the NCCO standalone database, not the Artemis platform, and click Explore Collections.

Data for research

Generally, scholars can download PDFs of documents from Artemis only one document at a time (up to 50 pages per download). When I asked about access to large amounts of data for use by digital humanists, the Gale representative said that while their databases are not built to be looked at on a machine level (because of the aforementioned bandwidth issues), Gale is beginning to provide data separately to scholars. They have a pilot program to provide datasets to Davidson College and the British Library, among others. Gale is also looking into setting up a new capability to share data that would be based outside their current system. The impression that I got was that they would be receptive to scholars who are interested in obtaining large amounts of data for research.

Bonus tip: direct (public) link to documents

Even though it doesn’t have anything to do with standards for presenting scholarship, I thought people might want to know about this handy feature. Artemis users have the ability to bookmark search results and save the URL for future reference. The link to the document(s) can then be shared with anyone, even those without logins to the database. To be clear, anyone that clicks on the link is taken directly to the document(s) although they won’t have the capability to extend the search. This makes it easy to share documents with students and through social media.

In this post, I have sought to shed some light on the usually opaque construction of proprietary databases. If people start “playing” with Artemis’ text mining lite capabilities, I would be interested in hearing about their perceptions of its usefulness for research.

Works cited

Jockers, Matthew L. “Theme.” Macroanalysis Digital Methods and Literary History. Urbana: University of Illinois Press. Print.

David Mimno and fatty tuna

David Mimno made an important distinction about theory vs. practice when he pointed out that MALLET (or any DH tool) is a method, not a methodology.  MALLET can uncover thematic patterns in massive digital collections, but it is up to the researcher using the tool to evaluate the results, pose new questions, and think of possible new uses for the tool.  In our class discussion, Mimno compared different roles in topic modeling to Iron Chef:  he makes the knives (MALLET), librarians dump a lot of fatty tuna (the corpus of text) on the table, and the humanists are the chefs who need to make the meal (interpreting and drawing new conclusions from the results).

As a librarian, I have never thought of myself as a provider of fatty tuna, but I get the general point. What role do librarians and other alt-academics play in DH? Can a librarian be a tool maker, a chef, a sous-chef, a waitress, or something else entirely?  What does it mean to curate content and devise valuable ways to access that content?  Is it scholarship? I am not sure if I can answer that question, but I do see many new ways to apply MALLET as a search and discovery tool which would be very useful for scholarship.

Can we do better than key word search to find relevant information in huge collections of digital text? Would search terms created from the body of the text itself be more accurate than hand-coding using the very dated and narrow Library of Congress subject headings? The DH literature on topic modeling doesn’t have much on libraries, but I did find the following information. Yale, U. Michigan, and UC Irvine received an Institute of Museum and Library Services grant to study Improving Search and Discovery of Digital Resources Using Topic Modeling. See also an interesting D-Lib Magazine article on using topic modeling in HathiTrust, A New Way to Find: Testing the Use of Clustering Topics in Digital Libraries  

The Twenty-First Century Footnote*

In Jefferson Bailey’s brilliant article on digital archives, he writes, “Digital objects will have an identifier, yes, but where they ‘rest’ in intellectual space is contingent, mutable. The key point is that, even at the level of representation, arrangement is dynamic . . . Arrangement, as we think of it, is no longer a process of imposing intellectualized hierarchies or physical relocation; instead, it becomes largely automated, algorithmic, and batch processed.”

Digital humanists have increasingly embraced text mining and other techniques of data manipulation both within bodies of texts that they control and in proprietary databases. When presenting their findings, they must also consider how to represent their methodology; to describe the construction of the databases and search mechanisms used in their work; and to make available the data itself.

Many people (Schmidt, Bauer, and Gibbs and Owens) have written about the responsibility of digital scholars to make their methods transparent and data publicly available as well as the need to understand how databases differ from one another (Norwood and Gregg).

Reading narratives of the research methods of David Mimno and Matt Jockers (as well as listening to Mimno’s recent lecture) has been useful for me in my ongoing thinking about the issues of how digital humanists use data and how they report on their findings. Mimno and Jockers are exemplars of transparency in their recitation of methods and in the provision of access to their datasets so that other scholars might be able to explore their work.

While every digital humanist may not use topic modeling to the extent that Mimno and Jockers do, it is fair to say that, in the future, almost all scholars will be using commercial databases to access documents and that that access will come with some version of  text analysis. But what do search and text analysis mean in commercial databases? And how should they be described? In relation to keyword searching in proprietary databases, the historian Caleb McDaniel has pointed out that historians do not have codified practices for the use and citation of databases of primary materials. He says that to correctly evaluate proprietary databases scholars should know whether the databases are created by OCR, what the default search conventions are, if the databases use fuzzy hits, when they are updated and other issues. At this time, much of the information about how they are constructed is occluded in commercial databases. McDaniel recommends the creation of an “online repository” of information about commercial databases and also suggests that historians develop a stylesheet for database citation practices.

Why is this lack of information about the mechanisms of commercial databases important? Because, as Bailey says, the arrangement of digital objects in archives (and databases) is automated, algorithmic, and batch processed. Yet, as historian Ben Schmidt has noted,  “database design constrains the ways historians can use digital sources” and  “proprietary databases “force” “syntax” on searches. Since database search results are contingent upon database structures, if scholars are making claims related to the frequency of search terms, at a minimum, they must understand those structures to reckon with the arguments that might be raised against their conclusions based on methodology.

I recently attended a presentation about a commercial database company’s venture into what I call “text mining lite.” What I learned has only bolstered my ideas about the importance of understanding the practices of proprietary database publishing and the necessity of scholars having access to that information. The company, Gale, one of the larger database publishers, seems to be courting the digital humanities community (or at least their idea of the digital humanities community). Gale is combining access to multiple databases of primary eighteenth and nineteenth century sources through an interface called Artemis which allows the creation of “term clusters.” These are clusters of words and phrases that occur a statistically relevant number of times within the user’s search results. One of the crucial things to know about the algorithms used is that Artemis term clusters are based on the first 100 words of the first 100 search results per content type. In practice, for search results that might include monographs, manuscripts and newspapers as types, this means that the algorithm runs only within the first one hundred words of the first one hundred monographs, the first one hundred words of the first one hundred manuscripts, and the first one hundred words of the first one hundred newspaper articles. [I will describe Artemis at more length in Part Two of this blog post.] Clearly, any conclusions drawn by scholars and others using term clusters in Artemis should include information about the construction of the database and the limitations of the search mechanisms and text analysis tools.

As a final project for the Digital Praxis Seminar, I am thinking about writing a grant proposal for the planning stages of a project that would consider possible means of gathering and making available information about the practices of commercial database publishers. I would appreciate any thoughts or comments people have about this.

* The title is taken from The Hermeneutics of Data and Historical Writing by Fred Gibbs and Trevor Owens. “As it becomes easier and easier for historians to explore and play with data it becomes essential for us to reflect on how we should incorporate this as part of our research and writing practices. Is there a better way than to simply provide the raw data and an explanation of how to witness the same phenomenon? Is this the twenty-first century footnote?”