The document explores various text analysis methods in digital humanities, particularly focusing on data narratives, exploratory data analysis, and the importance of understanding inherent biases in historical data collection. It emphasizes the complementary role of humans and automated methods in analyzing texts while outlining practical resources, data management, and analysis tools available for researchers. Key processes like data formatting, validation, and entity extraction are discussed alongside specific tools and frameworks to aid in these analyses.

1. Text Analysis Methods
for Digital Humanities
Helen Bailey and Sands Fish
MIT Libraries

2. Examples of Data Narratives
• Visualizing Emancipation
• Narrative Visualization of Whaling Ship Logs
• Out of Sight, Out of Mind

3. Approaches to Storytelling w/ Data
• EDA - Exploratory Data Analysis
• Exploring data from a number of perspectives:
o Temporal
o Geographical
o Statistical
o Categorical
o Relational
• 80% - Data Hacking, 20% - Narrative Construction, Visualization,
etc.

4. "To use any sort of historical data, we must above all understand the
constraints under which it was collected. In this case, that means
retelling the history of why and how the ship's logs were first collected, and
how the constraints of digitization in the punch card era radically shape the
sort of evidence we can draw from them. The important thing about this sort
of work is that it helps us understand the overall biases of a particular
data set, which is crucial for limiting our interpretive leaps."
- Ben Schmidt, “Reading digital sources: a case study in ship's logs”

5. Inherent Biases & Limitations
• Data capture methods and format
• Purpose of data collection
• Transformation over time
• Authenticity and trust
Understand provenance

6. “Rather than replace humans, computers amplify human abilities. The
most productive line of inquiry, therefore, is not in identifying how automated
methods can obviate the need for researchers to read their text. Rather, the
most productive line of inquiry is to identify the best way to use both
humans and automated methods for analyzing texts.”
- Justin Grimmer and Brandon M. Stewart, “
Text as Data: The Promise and Pitfalls of Automatic Content Analysis
Methods for Political Texts”

7. Acquiring Text
• Full-text resources:
o DSpace@MIT http://dspace.mit.edu/
o Dome http://dome.mit.edu/
o Digital Public Library of America http://dp.la
o Europeana http://www.europeana.eu/portal/
o HathiTrust http://www.hathitrust.org/
• http://libguides.mit.edu/apis - metadata only
• http://libguides.mit.edu/digitalhumanities

8. Data Management and Sharing
• Assumption of sharing and data management plan as a
funding requirement
• Data storage options - anticipate interaction
o Storage formats - non-proprietary and repurposable
whenever possible
o File system storage vs. database
• Documentation of process
http://libraries.mit.edu/guides/subjects/data-management/

9. Formatting / Pre-Processing
• Tool input requirements
• Assumptions:
o Text as a “bag of words”
o Unigrams, bigrams
o Word order (or not)
o Stop words, capitalization, punctuation

10. Featurizing Text
• Each word becomes a feature
• This is called "high dimensional" data
• Each word is a "dimension", or "feature"
• Features are represented as vectors in Euclidean space
• Euclidean mathematics scales beyond 3 dimensions

11. The Shape of Data
• Data structures and formats
• Informed (in part) by:
o Tools
o Co-occurrence
o Data output formats
o Entity type
o Temporal, geographical perspective, etc.

12. Validation
From Ben Schmidt’s “Machine Learning at Sea”

13. Network Models
• Representing data as a network
o Types: technological, communication, transportation, energy, airplane routes,
web linking patterns
o social
§ non-human animal interaction
§ membership in larger groups
§ sexually transmitted diseases
§ co-authorship of scientific publications
§ trade agreements between nations
• Mapping the News - Berkman's Controversy Work
o Spidering
o Influential actors over time

14. Topic Modeling Tools
• MALLET
o Can run on unstructured plain text files
o http://mallet.cs.umass.edu/topics.php
• Stanford Topic Modeling Toolbox
o Requires data in a CSV or TSV file
o http://nlp.stanford.edu/software/tmt/tmt-0.4/

15. Entity Extraction
• Identifies known entities in specific categories
o Locations
o People
o Organizations
o Dates/times
• Creates annotated text from unstructured text
• Domain-specific

16. Entity Extraction Tools
• Stanford Named Entity Recognizer
http://nlp.stanford.edu/software/CRF-NER.shtml
• Illinois Named Entity Tagger
http://cogcomp.cs.illinois.edu/page/download_view/NETagger
• DBPedia Spotlight
https://github.com/dbpedia-spotlight/dbpedia-spotlight/wiki

17. Geo-Parsing
• Common Pitfalls
o Set of places (GeoNames dictionary)
o Dictionary determines how broad or narrow your
search is
• Enhancements to CLAVIN by Civic Media
o Aboutness (uses mention counting)
o HTTP access used for more advanced workflows