This document discusses the quantification and analysis of qualitative data using APIs, highlighting the differences between qualitative and quantitative research methods. It provides examples of how machine-readable data can be acquired from various platforms and how it can be analyzed for insights from non-numerical data. Key takeaways include the potential to analyze non-numeric data at scale and the automation of the scientific workflow through the strategic use of APIs.

1. Quantifying Qualitative Data
– Big Time!
Jukka Sihvonen©
Department of Accounting and Finance

2. Workflow
Data acquisition
Data analysis
Results
Quantitative
- Replicable
- Scalable
- Easily revisable
Qualitative
- Not replicable
- Not scalable
- Revision is hard
Financial research Literary research
month(s)
month(s)
minute(s)
minute(s)
Service
Jukka Sihvonen© | Big Data and Digitalization

3. Analysis
at the office
by hand
Qualitative data
- Photos
- Movies
- Speech
- Text
Results
Traditional way
Jukka Sihvonen© | Big Data and Digitalization

4. Application Programming Interface (API)
Qualitative data
- Photos
- Movies
- Speech
- Text
Data API Analysis API
Intelligence API
Data refining API
Results
Replicability – Speed – Possibilities
Jukka Sihvonen© | Big Data and Digitalization

5. Data APIs – retrieve machine readable data
Examples
- Wikipedia
- Facebook
- Twitter
- Instagram
- Suomi24
- Yle
- Google Maps
- Accuweather
- Scopus
import twitter
api = twitter.Api(my_credentials)
tweets = api.GetSearch(term = “univaasa”, count = 10)
Request …
... Response
Twitter API
Twitter example: @univaasa
Jukka Sihvonen© | Big Data and Digitalization

6. Data refining APIs – metadata and converting
Examples
- Voice to gender
- Names to ethnicity
- Novel to characters
- Articles to abstracts
- Email to language
- Speech to text
- Photo to text
- HTML to text
- PDF to text
Microsoft Cognitive API
Jukka Sihvonen© | Big Data and Digitalization

7. Analysis APIs – insights from non-numerical data
Examples
face to emotion, speech to person, diary to personality,
story to concept, correspondence to tone
anger: 0.00,
contempt: 0.00,
disgust: 0.00,
fear: 0.00,
happiness: 0.73,
neutral: 0.26,
sadness: 0.00
surprise: 0.00
Emotions…
This is my rifle. There
are many like it, but
this one is mine.
My rifle is my best
friend. It is my life. I
must master it as I
must master my life.
My rifle, without me,
is useless...
Personality…
inner-directed,
strict,
shrewd,
skeptical,
restrained
Our Father,
who art in heaven,
hallowed be thy name,
thy kingdom come,
thy will be done,
on earth as it is in
heaven.
Give us this day our
daily bread and
forgive us our debts…
Concepts…
Christianity
Christian prayer
Linguistics
Gospel of Matthew
Lord's Prayer
Me! Rifleman’s Creed Lord’s Prayer
Jukka Sihvonen© | Big Data and Digitalization

8. Intelligence APIs – teach machine to classify
Three Musketeers Moby Dick Crime and Punishment
Count de Rochefort Ishmael Dmitri Razumikhin
Monsieur Bonacieux Captain de Deer Andrei Lebezyatnikov
Duke of Buckingham Dough Boy Nastasya Petrovna
Bazin Starbuck Porfiry Petrovich
Felton Ahab Katerina Marmeladov
Anne of Austria Flask Pyotr Luzhin
Athos Fedallah Pulcheria Raskolnikov
Kitty Stubb Alexander Zamyotov
Constance de Bonacieux Captain Bildad Ilya Petrovich
Planchet Queequeg Rodion Raskolnikov
Monsieur de Tréville Daggoo Lizaveta Ivanovna
Milady de Winter Elijah Sofya Marmeladov
Porthos Captain Peleg Alyona Ivanovna
Character Which book? Accuracy
D'Artagnan ?
Grimaud ?
Aramis ?
Mousqueton ?
Captain Boomer ?
Father Mapple ?
Tashtego ?
Pip ?
Semyon Marmeladov ?
Arkady Svidrigailov ?
Zossimov ?
Avdotya Raskolnikov ?
IBM
Watson
Training data Testing data
Character Which book? Accuracy
D'Artagnan Three musketeers True
Grimaud Three musketeers True
Aramis Three musketeers True
Mousqueton Three musketeers True
Captain Boomer Moby Dick True
Father Mapple Moby Dick True
Tashtego Moby Dick True
Pip Moby Dick True
Semyon Marmeladov Crime and punishment True
Arkady Svidrigailov Crime and punishment True
Zossimov Moby Dick False
Avdotya Raskolnikov Crime and punishment True
Jukka Sihvonen© | Big Data and Digitalization

9. Scaling up – literary research
For Each Book in Library:
For Each Page in Book:
Result(Page) <= Send(Page, API)
Next Page
Next Book
Library none
Book The Diary of a Young Girl
API Watson Sentiment Analysis
For-loop that sends textual material to API:
Empirical exercise: Anne Frank’s diary
Cumulative Standardized Sentiment 1942 – 1944
Fritz Pfeffer
joins the annex
“The sun is
shining … I think
spring is inside
me. I feel spring
awakening”
Jukka Sihvonen© | Big Data and Digitalization

10. Scaling up – communication studies
For Each Movie in Collection:
For Each Frame in Movie:
Result(Frame) <= Send(Frame, API)
Next Frame
Next Movie
Collection none
Movie Final presidential debate
API Microsoft Emotion API
For-loop that sends images to API:
Empirical exercise: Trump-Clinton Debate
Trump’s primary
facial expression
is angry, and
more so if not
having the floor
Hillary emphasizes
message by raising
eyebrows, expresses
integrity by smiling
Jukka Sihvonen© | Big Data and Digitalization

11. Key takeaways
- Non-numeric data can be obtained and analyzed at an industrial scale
- The scientific workflow can be automated by chaining APIs cleverly
- Huge implications on what can be researched and at what cost
Jukka Sihvonen© | Big Data and Digitalization