Topic Modelling

Uncovering New Angles and Arguments with Text Mining

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│By Georgia Platt, Gale Ambassador at Lancaster University│

Six months ago, I knew nothing about Digital Humanities or text mining. It wasn’t really a topic covered in my undergraduate degree in History, and I’d always been a bit unsure of the place technology has in historical research. Then I began my MA in Digital Humanities, which I chose to try and engage with these technological aspects of humanities studies that I hadn’t had the opportunity to explore before. I spent the first ten weeks of this course doing an intensive text mining module, and it revolutionised my approach to historical study.

If you’re curious about text mining but don’t know where to start, or are a digital humanities sceptic looking to be proved wrong, this blog post is for you. I will start with some basic theory of what text mining is and what kinds of questions it can answer, then I’ll put my practical skills to the test with an example from my studies in Victorian crime and punishment.

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Gale Fellows Present their DH Research at the Socio-Economic History Society Conference

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Blog Post Author with the Gale–SEHS Fellows

| By Masaki Morisawa, Product Solutions Consultant | On the sunny weekend of 9–10 May 2026, the 95th National Conference of the Socio-Economic History Society (SEHS) took place at Senshu University’s suburban Ikuta campus. More than 300 members gathered for the event, presenting and discussing topics as varied as promissory notes in Edo-period Japan, American … Read more

Understanding Recent Enhancements to Sentiment Analysis in Gale Digital Scholar Lab

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│By Sarah L. Ketchley, Senior Digital Humanities Specialist│

Sentiment Analysis can be described as an exploratory form of analysis that reveals trends or patterns in positive, negative or neutral sentiment of a collected set of documents (a ‘Content Set’). This type of analysis can identify avenues for further research or investigation at both a macro (Content Set) and micro (document) level.1 Sentiment Analysis is one of the six analysis tools available in Gale Digital Scholar Lab where it was recently updated to include an expanded sentiment lexicon which offers opportunities to further refine analysis results. This blog post will discuss these enhancements and offer suggested pathways to work with Sentiment Analysis both in the classroom and for research.

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Digging into Datasets in Gale Digital Scholar Lab

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│By Sarah L. Ketchley, Senior Digital Humanities Specialist, Gale│

This dataset post is a follow-up to Working with Datasets, a Primer which discussed text datasets of primary sources and explored how to access and work with them in Gale Digital Scholar Lab. Here, we’ll look at the topics of the first eight datasets in the Lab in more detail, the types of documents included in each set, and consider how a user may work with them for analysis. Our next blog post will showcase classroom-based use of the Lab’s datasets as an introductory pathway into the field of digital humanities.

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Taking Your Master’s Dissertation to the Next Level: Using Gale Digital Scholar Lab for Research

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Gale Egyptology sentiment analysis

│By Tamar Atkinson, Gale Ambassador at the University of Liverpool│

Primary sources can be a great resource for Master’s dissertation research, providing a deeper understanding of history. Whilst they make handy supportive evidence to back up the points you want to make in an assessment, is there a way to take them further? Within Gale Digital Scholar Lab, you can find a whole range of data-mining visualisation tools and other resources that can allow you to bring elements of Digital Humanities methodologies into your research, through an easy, step-by-step process. This can add great insights to any work!

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Digital Humanities and Data Mining: The Key to Efficient Seminar Preparation

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Header image - piles of leather bound books with spines facing outwards

│By Becca Leeland, Gale Ambassador at the University of Exeter│

Anyone that says to a humanities student ‘all you have to do is read a book and have an opinion’ is someone who’s never had to prepare for a seminar! Seminars are the foundation of my degree – they guide me to the crucial works in a given field, they give me the space to explore ideas, and sharpen my skills in argument and writing. But they can also be daunting, especially when you’re presented with the work of the finest minds in history and are expected to make sense of it; you know you’re intelligent, you know you can do this, but sometimes you just need a little push in the right direction…

*Enter Gale Digital Scholar Lab.*

Gale Digital Scholar Lab is designed to make data mining and primary research more efficient with a three-step process of build, clean, and analyse. Utilising some of these techniques, you can gain a solid idea of where the texts are going and what to look out for, something that is really helpful when you start reading the material more closely.

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Separating the Wheat from the Chaff: Identifying Themes and Topics in Archives Using Gale Digital Scholar Lab

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Notes from our DH Correspondent

│By Sarah L. Ketchley, Senior Digital Humanities Specialist│

Getting to grips with the scope and content of a digital primary source archive held by an institution’s library can be daunting, particularly if the archive consists of thousands of documents in a variety of formats. For an individual researcher, the task of sifting through vast quantities of data in the quest for material that is relevant for a particular research topic is something that can take years to accomplish. This blog post will explore some of the ways a researcher can use Gale Digital Scholar Lab in conjunction with Gale Primary Sources as a platform for exploratory analysis to gain insights into the topics and themes represented in a chosen archive.

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Introducing My Students to Digital Humanities Research Techniques

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Woman working on laptop

│By Ben Wilkinson-Turnbull, Gale Ambassador at the University of Oxford│

Digital resources are vital to conducting academic research and teaching the next generation of scholars. As educators, teaching with technology can be daunting. In my previous blog posts PhDing in a Pandemic: The Impact of COVID-19 on Research and Teaching and Top 10 Tips for Teaching with Primary Sources, I’ve written about how you can help students get to grips with using a range of Gale Primary Sources including Eighteenth Century Collections Online, Burney Newspapers, and British Literary Manuscripts Online. But how do you help your students take the next step as digital humanists in a growing discipline? Teaching them how to use an innovative resource such as Gale Digital Scholar Lab is one way which you as an educator can help students develop their research skills and methodologies in a changing scholarly landscape.

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Doing the Digital Laundry? Notes on Cleaning Unstructured Text Data

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Notes from our DH Correspondent

│By Sarah L Ketchley, Senior Digital Humanities Specialist│

An integral part of the workflow of any digital humanities project involving text generated automatically by Optical Character Recognition (OCR) is the correction of so-called OCR errors. The process is also called ‘data cleaning’. This post will explore some of the considerations researchers should be aware of before starting to clean their data in Gale Digital Scholar Lab, using the built-in text cleaning tool. It will also offer additional resources for working with data in other formats outside of the Lab.

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Creating an Export Workflow with Gale Digital Scholar Lab

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Notes from our DH Correspondent

│By Sarah L. Ketchley, Senior Digital Humanities Specialist│

This digital project was prompted by the broad research question: how was archaeology reported in The Illustrated London News (ILN)? The ILN is a publication notable for its fine illustrations and contributions by some of the pre-eminent archaeologists of the day. Gale Primary Sources offers access to the entire run of the newspaper covering the period 1842-2003. This blog post describes a workflow for the preliminary investigation of the data: initial content set creation, cleaning, analysis, export and visualization. At the outset, the research questions were necessarily broad:

  • Which words were most prevalent in articles reporting on archaeological digs?
  • What themes or topics are most prevalent in the dataset?
  • What was the overall feeling about this type of reporting? Was it reported favourably?
  • Is it possible to identify which archaeologists were directly contributing to the publication and how many contributions they made?

Engaging in the practical process of curation and analysis offers opportunities to refine these questions, and almost inevitably suggests new avenues for future exploration.

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