Explore Text Mining through Student Research

 

Gale Digital Scholar Lab brings the power of large-scale analysis of primary sources to any researcher, regardless of their technical ability. By making discoveries through data analysis, learn how you can create new knowledge and develop crucial digital skills and literacies.

The projects below show students unleashing their creativity and exploring subjects from innovative perspectives using the Lab. Use the Showcase to get ideas and guidance for developing your own project in Gale Digital Scholar Lab.

Research, collaborate on, create and submit your project in Gale Digital Scholar Lab. Contact your library or search here to see if you have existing access. Library and academic staff can request a trial here.  

For further information on how to build and submit your project, see the Gale Research Showcase FAQs.  

Current Projects by Subject

Projects by Analysis Type

DOCUMENT CLUSTERING

Create clusters of similar documents through analysis of their textual attributes.
 

NAMED ENTITY RECOGNITION

Identify and extract Named Entities (people, places, currencies etc) from within documents.

NGRAMS

Identify and visualise the frequency of words or phrases within a Content Set.

PARTS OF SPEECH

Use natural language processing of syntax to recognise and tag parts of speech.

TOPIC MODELING

Create groups of words in a Content Set that are statistically more likely to occur near to each other.

SENTIMENT ANALYSIS

Classify documents based on the sentiment of the words contained within them.

REQUEST A FREE TRIAL   |   CONTACT YOUR LOCAL GALE REPRESENTATIVE   |   SUPPORT AND TRAINING

ABOUT GALE PRIMARY SOURCES   |   GALE DIGITAL SCHOLAR LAB   |   THE GALE REVIEW BLOG

DISCLAIMER

Any views and opinions expressed in these essays are those of the author in question, and any views or opinions from the original source material are those of the publication in question. Gale, part of Cengage Group, provides facsimile reproductions of original sources and do not endorse or dispute the content contained in them. Author affiliation and information within them are correct as of the original publication date.