Computational Text Analysis and Social Media Research using R

Computational Text Analysis and
Social Media Research using R

Justin Chun-ting Ho

Learning Objectives:

Basic R (Day 1 & 2)
Collecting social media data (Day 3)
Analysing text using R (Day 4 & 5)

(Realistic) Learning Objectives:

By the end of the course, you will be able to:

Know what software and packages are available for various research tasks
Copy and paste a chunk of codes and make it work
Read documentation and R scripts so that you can further your learning independently

Introduction to R

R & R Studio

Create a new project

Under the File menu, click on New project, choose New directory, then New project
Enter a name for this new folder, and choose a convenient location
Click on Create project
Create a new file where we will type our scripts

Interaction with R

Type commands directly into the console and press 'Enter'
Execute commands directly from the script editor using 'Ctrl' + 'Enter' ('Cmd' + 'Return' for Macs)

Tidyverse

%>%

Grammar of Graphics

Collecting Social Media Data

What is an API?

Documentations:

Facebook

Twitter

Warning!

API is NOT designed for research's purposes!
What you can get depends on the platform's mercy!
It is prone to error and bias!

Available Endpoints and Limitations

Facebook:

Graph API: Public pages data only (no personal profile); Maximum of 600 posts per page per year; Known bias exists (See: Ho, 2020)
CrowdTangle: Public Facebook pages and Instagram accounts; Limitations on type of data; Available to university researchers on selected topics only

Twitter:

Search API: Limited to tweets published in the past 7 days
Streaming API: ~2% of Twittersphere; Known bias exists (See: Morstatter et al., 2014)
Academic Research Product Track: Available to academic researchers only; Full-archive access; Sounds too good to be true

Conclusion:

Know what you can (can't) get
Collect data while you still can
(it might not be the case tomorrow)
Observe Terms of Service
(and other platform's restrictions)

Time for Data Collection

Text Analysis: The Basics

“Systematic, objective, quantitative analysis of message characteristics"

Kimberly A. Neuendorf, The Content Analysis Guidebook

Types of Text Analysis

Degree of human involvement:

Human coding (100%)
Supervised
Unsupervised (0%)

Type of Objective:

Scaling
Classification

(Source: Justin Grimmer and Brandon Stewart, 2013)

4 Principles of Text Analysis

(Read: Grimmer, J. and Steward, B. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267-297. doi:10.1093/pan/mps028)

1. All Quantitative Models of Language Are Wrong —But Some Are Useful

Data generation process for any text is a mystery
All methods necessarily fail to provide an accurate account of the data-generating process
Meanings change drastically: “Time flies like an arrow. Fruit flies like a banana.”

2. Quantitative Methods Augment Humans, Not Replace Them

Text Analysis will not eliminate the need for careful thought nor remove the necessity of reading
Rather than replace humans, computers amplify human abilities

3. There Is No Globally Best Method for Automated Text Analysis

Different research questions and designs need different models
The same model will perform well on some data sets, but poorly on other

4. Validate, Validate, Validate

Results can be misleading or simply wrong
Supervised methods: able to reliably replicate human coding
Unsupervised methods: the measures are as conceptually valid

Bag of Words Assumption

- Word order doesn’t matter

- The followings mean exactly the same:

I enjoy eating food and being with my family
I enjoy eating my family and being with food
and being eating enjoy family food I my with

Original Text

## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 181
##
## Article 1. All human beings are born free and equal in dignity and rights. They are endowed with reason and conscience and should act towards one another in a spirit of brotherhood.

Remove Punctuation

## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 178
##
## Article 1 All human beings are born free and equal in dignity and rights They are endowed with reason and conscience and should act towards one another in a spirit of brotherhood

To Lower Case

## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 178
##
## article 1 all human beings are born free and equal in dignity and rights they are endowed with reason and conscience and should act towards one another in a spirit of brotherhood

Remove Numbers

## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 177
##
## article  all human beings are born free and equal in dignity and rights they are endowed with reason and conscience and should act towards one another in a spirit of brotherhood

Remove Stopwords

## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 135
##
## article   human beings  born free  equal  dignity  rights   endowed  reason  conscience   act towards one another   spirit  brotherhood

Stemming

## <<PlainTextDocument>>
## Metadata:  7
## Content:  chars: 108
##
## articl human be born free equal digniti right endow reason conscienc act toward one anoth spirit brotherhood

Optional: Create N-Gram


##
## article_1 1_all all_human human_beings beings_are are_born born_free free_and and_equal equal_in

Create DFM


##     Terms
## Docs act anoth articl born brotherhood conscienc digniti endow equal free
##    1   1     1      1    1           1         1       1     1     1    1
##    2   0     0      1    0           0         0       0     0     0    1

The Output

Time for R

Keyword Analysis

What is a Keyword?

“A keyword may be defined as a word which occurs with unusual frequency in a given text. This does not mean high frequency but unusual frequency, by comparison with a reference corpus of some kind”  

(Scott, M. (1997). PC analysis of key words - and key key words. System, 25(2), 233-45.)

Keyword Analysis

What is Keyness?

“The keyness of a keyword represents the value of log-likelihood or Chi-square statistics; in other words it provides an indicator of a keyword’s importance as a content descriptor for the appeal.”

(Scott, M. (1997). PC analysis of key words - and key key words. System, 25(2), 233-45.)

Keyword Analysis

What is a Chi-squared test?

Comparison between the observed frequency and expected frequency.

Time for R

Sentiment Analysis

What is Sentiment Analysis?

Sentiment analysis, also called opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes.

What is Sentiment?

Opinions (Good vs Bad)
Emotions (Happy vs Sad)
Attitudes (Like vs Dislike)

Levels of Analysis

- Document level

- Sentence level

- Entity and Aspect level

Examples of Usage

- Product Review

- Public Opinion

- Voters Support

- Wellbeing/Mental Health

Lexicons

- "Dictionary" for sentiment

- Popular Lexicons:

LIWC
Lexicoder Sentiment Dictionary (postive, negative)
AFINN (postive to negative from +5 to -5)
Bing (postive, netgative)
NRC (postive, netgative, anger, anticipation, disgust, fear, joy, sadness, surprise, trust)

Challenges

1. Opposite orientations in different applications.

“This camera sucks.” vs
“This vacuum cleaner really sucks.”

Challenges

2. Sentence containing sentiment words may not express any sentiment.

“If I can find a good camera in the shop, I will buy it.”

Challenges

3. Sarcastism

“What a Genius! You uploaded your passwords to Github!”

Note: Very common in political discussion, especially on social media.

Challenges

4. Sentences without sentiment words can also imply opinions.

“This car burns a lot of fuel.”