ananse 1.1.2

A python package to partially automate search term selection and writing search strategies for systematic reviews

Ananse

This project is a collaboration between Dr. Effah Antwi,Research Scientist, Natural Resources Canada, Canadian Forest Service and Dr Wiafe Owusu-Banahene, Department of Computer Engineering, School of Engineering Sciences, University of Ghana, Legon, Accra, Ghana. The Ananse package is a python package designed to partially automate search term selection and writing search strategies for systematic reviews. Read the documentation at baasare.github.io/ananse and ananse.readthedocs.io/

Setup

Ananse requires python 3.7 or higher

Using pip

pip install ananse

Directly from the repository

git clone https://github.com/baasare/ananse.git
python ananse/setup.py install

Quick start

Writing your own script

from ananse import Ananse

min_len = 1 # minimum keyword length
max_len = 4 # maximum keyword length


# Create an instance of the package
test_run = Ananse()


# Import your naive search results from the current working directory
imports = test_run.import_naive_results(path="./")

# Columns to deduplicate imported search results
columns =  ['title', 'abstract']


#de-duplicate the imported search results
data = test_run.deduplicate_dataframe(imports, columns)


#extract keywords from article title and abstract as well as author and database tagged keywords
all_terms = test_run.extract_terms(data, min_len=min_len, max_len=max_len)


#create Document-Term Matrix, with columns as terms and rows as articles
dtm, term_column = test_run.create_dtm(data.text, keywords=all_terms, min_len=max_len, max_len=max_len)


#create co-occurrence network using Document-Term Matrix
graph_network = test_run.create_network(dtm, all_terms)


#plot histogram and node strength of the network
test_run.plot_degree_histogram(graph_network)
test_run.plot_degree_distribution(graph_network)


#Determine cutoff for the relevant keywords
cutoff_strengths = test_run.find_cutoff(graph_network, "spline", "degree", degrees=3, knot_num=1, percent=0.879956,
                                            diagnostics=True)


#get suggested keywords and save to a csv file
suggested_keywords = test_run.get_keywords(graph_network, "degree", cutoff_strengths, save_keywords=True)


#Print suggested keywords
for word in suggested_keywords:
   print(word)

Using Ananse Test Script

python tests/ananse_test

References

This is a python implementation of the R package as mentioned in paper An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks by Eliza M. Grames, Andrew N. Stillman Morgan W. Tingley and Chris S. Elphick