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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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Groups and Notebooks: Using Gale Digital Scholar Lab’s latest features in the DH classroom

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Notes from our DH Correspondent

│By Sarah Ketchley, Senior Digital Humanities Specialist│

The field of digital scholarship tends to be collaborative, since any given project may involve disciplinary experts, developers, librarians, archivists, and students. Management of workflow and data can be challenging unless there is careful planning from the outset about record-keeping, group working practices, the sharing of information and goals for project sustainability and longer-term archiving. These practical considerations are the same for research projects and for those built in the classroom.

The ability to create Groups was recently added as a feature to the Gale Digital Scholar Lab platform, along with a flexible ‘Notebook’ tool for documenting decisions and outcomes. This blog post will consider how Group spaces can be used to facilitate classroom project-building by students in an undergraduate classroom, using a recent course I taught in the Information School at the University of Washington as a case study. The practicalities of using the Groups/Notebook features were discussed in my previous blog post, including details about how a teacher might go about adding students to new groups within the Lab, then managing classroom workflow via record-keeping in the team’s Notebook.

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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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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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