Tutorial 03: 1D Counts

Collect term occurrence data from the scientific literature.

Term Occurrence

One thing we may want to examine in the literature is how common certain research topics are.

To do so, we can search for term occurrence - how often particular search terms occur in the the literature. This will be the topic of the current tutorial.

# Import the Counts1D object, which is used for co-occurrence analysis
from lisc import Counts1D

Counts1D Object

The Counts1D object is used to handle term occurrence collection and analyses.

This object is based on the Base object introduced in the prior tutorial, meaning we can define and add terms to it like introduced there.

# Set up some terms to search for
terms = [['frontal lobe'], ['temporal lobe'], ['parietal lobe'], ['occipital lobe']]

# Initialize counts object & add the terms that we want to collect co-occurrences for
counts1d = Counts1D()
counts1d.add_terms(terms)

As before, we can check the terms that we’ve added to the object.

Note that adding terms could also include adding synonyms, inclusion, and exclusion terms (see previous tutorials).

# Check terms definition
counts1d.check_terms()

List of terms used:

frontal lobe    : frontal lobe
temporal lobe   : temporal lobe
parietal lobe   : parietal lobe
occipital lobe  : occipital lobe

Now we are ready to run collection!

# Collect term occurrence data
counts1d.run_collection(verbose=True)

Running counts for:  frontal lobe
Running counts for:  temporal lobe
Running counts for:  parietal lobe
Running counts for:  occipital lobe

We have now collected some literature data!

The Counts1D object will now contain data on term occurrence in the literature.

This is stored in the counts attribute of the object.

We can check the results of the occurrence search with the check_counts method.

# Check counts per term
counts1d.check_counts()

The number of documents found for each search term is:
  'frontal lobe'     -   17549
  'temporal lobe'    -   34937
  'parietal lobe'    -    6507
  'occipital lobe'   -    4995

If we want to compare term occurrences, we can also use the check_top method to check the term with the most occurrences.

# Check top term
counts1d.check_top()

The most studied term is  'temporal lobe'  with  34937  articles.

By itself, term occurrence provides some insight into the literature, in a simple way.

In the next tutorial we will build up from this simple term occurrence to examine term co-occurrence, allowing for examining more complex questions about how different research topics relate to each other.