Quantitative text analysis

Question 1

What is quant text analysis

Answer 1

Converting text into numerical values and use statistical analysis to identify patterns, trends & relationships within the text.

Question 2

Computational text analysis

Answer 2

Using automated and semi-automated computational techniques to process, analyze & interpret textual data.

Question 3

KWIK

Answer 3

Key words in context.
Return a list of the keyword, identifying the source text and the word index number within the source text.
In which context do key words appear?
Can be used to identify and extract paragraphs of interest.

Question 4

Keyness

Answer 4

Compare the differential associations of keywords in a target and reference group.
Which words are used more by one group, relative to the other one?
The most common words are often similar, but the focus if on the words that distinguish between the two groups.

Question 5

Lexical dispersion plot

Answer 5

Visualize the occurrences of particular terms throughout the text.

Not only how often the term is used, but also WHERE in the speech it is used

Question 6

Lexical diversity

Answer 6

Measure of how many different words are used in a text
How rich is the vocabulary?

Question 7

Lexical density

Answer 7

Measure of the proportion of lexical items (i.e. nouns, verbs, adjectives and some adverbs) in the text.
How complex is the text itself?

Question 8

Co-occurrence

Answer 8

• Measuring co-occurrences of features within a user-defined context.
  A document
  A window within a collection of documents
  Can be plotted as a co-occurrence network.

Question 9

What is a dictionary

Answer 9

Dictionary – exclusive – one feature linked to one key

Thesaurus – not exclusive – set of features linked to one key

Question 10

LIWC: Linguistic Inquiry and Word Count

Answer 10

• Uses a dictionary to calculate the percentage of words in the text that match each of up to 82 language dimensions

Question 11

Coding scheme in dictionary

Answer 11

Hierarchy
First level - domain
Second level - subdomain
Other levels: may be additional sub-domains.

Question 12

What are dictionaries for

Answer 12

Describe the text
Measure expressed concepts in documents
Identify words that separate different categories, such as policy categories
Measure how often the categories apply in the text

Question 13

Supervised classification

Answer 13

Manually code a subset of the data
Use a supervised classifier to learn the relation between the words and the labels/categories.
Infer labels for the rest of the dataset.

Question 14

Unsupervised classification

Answer 14

Discover the main themes/topics in an unstructured corpus
- Infer hidden variables

Question 15

Structurual topic model

Answer 15

How are some covariates associated with the prevalence of topic usage?