About this project¶

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](https://baasare.github.io/ananse/_build/html/index.html) and [ananse.readthedocs.io/](https://ananse.readthedocs.io/en/latest/)

Preparing a Search Strategy for a Systematic Review¶

Report by:¶

Bernard Atiemo Asare
Amma Frimpomaa Frimpong-Boateng

Writing the Naïve Search and Importing the Results:¶

[1]When writing a naive search, the first step is to clearly articulate the research question. This serves as the basis for identifying concept groups and naive search terms. Using naïve search, a Boolean search was performed to return significant articles. The naive search entails relevant search terms grouped into concept categories, or sets of synonymous terms. With our project we grouped the search terms into four concept categories and then combine them into a Boolean search. If imprecision of the naïve search is high, irrelevant keywords would be identified. This imprecision may also strain the keyword selection process leading to the selection of unnecessary and vague terms.

Assembling and deduplicating results¶

In searching for information many articles having similar contents may pop up from multiple databases with some appearing more than once. Naïve search results need to be assembled and deduplicated to prevent this over-representation. Due to the different formats in the exportation results from databases this manual process takes more time. For our project we considered two databases; Web of science with a .csv file extension and Scopus with a .txt file extension. Provided the path to the directory of search results is given, the import_naive_results function automatically finds the database and file type of each file, selects analogous columns and joins them to form a single dataset. This function imports the search results from a specified path. If the parameters clean_dataset and save_dataset are set to true, then the function de-duplicates search results after importing and saves the full search results to a .csv respectively. The parameter save_directory contains the path to a directory where search results will be saved if save_dataset is set to TRUE while the parameter path takes the path containing the naive search results files. After the results are obtained a pandas data frame consisting of assembled search results is returned.

Keyword Extraction¶

Extracting and Identifying Keywords [2]Rapid Automatic Keyword Extraction (RAKE) is a well-known keyword extraction method which uses a list of stopwords and phrase delimiters to detect the most relevant words or phrases in a piece of text. The function extract_terms terms calls the RAKE algorithm and eliminates keywords that only appear in a single article, and excludes phrases with only one word from the list of potential keywords resulting in a more precise search. Then with our method, the author- and database-tagged keywords are combined with the search terms.

Co-occurrence Network¶

The author tagged and database tagged keywords are combined as dictionary object created with extract_terms to define all possible keywords. All the possible keywords are then passed to the function create_dtm. [3]Then there is a generation of a keyword cooccurrence network where each node represents a potential search term and the edges arc co-occurrences of two terms in the title, abstract, or tagged keywords of a study, with the function create_network which measures the importance of each term in relation to the selected topic being reviewed. Node strength, a weighted measure of the degrees of a network that indicates how well-connected a node is to other strong nodes is the default attribute used by litsearchr is the measure of node importance. The function get_centrality is used to rank nodes by strength and using genetic algorithm finds knots since there are regions of rapid change where nodes become increasingly more important due to the fact that the node strength of a network tends to follow a power law.

Identifying Important nodes using the full network¶

There are two methods to identify important nodes in the litsearchr package:

1)Fitting a spline model to the node importance to select tipping points

2)Finding the minimum number of nodes to capture a large percent of the total importance of the network.(Cumulative Approach)

Depending on the distribution and preference one can decide which method to use. In choosing a method the first thing to do is to look at the distribution of node importance. The distribution can be plotted with the function plot_degree_distribution, plot_rank_degree_distribution or plot_degree_histogram

A spline model for finding cutoff is an appropriate method to identify the cutoff threshold for keyword importance if the rank distribution plot has a lot of fairly weak nodes with a long tail.The cumulative approach is more appropriate when there are no clear breaks in the data.

The find_cutoff function finds the cutoff for a graph network using either cumulative or spline method of cutting of the degree distribution. Then the reduce_graph function generates a graph consisting of only important nodes after which the get_keyword function extract the keywords from the reduced network.

For this project, the databases supported were mainly Web Of Science and Scopus.

[1]E. Stillman, “Introduction to litsearchr with an example of writing a systematic review search strategy for black-backed woodpecker occupancy of post-fire forest systems”, Elizagrames.github.io, 2020. [Online]. Available: https://elizagrames.github.io/litsearchr/introduction_vignette_v010.html. [Accessed: 08- Jan- 2020].

[2]”Keyword Extraction”, MonkeyLearn, 2020. [Online]. Available: https://monkeylearn.com/keyword-extraction/. [Accessed: 08- Jan- 2020].

[3]2020. [Online]. Available: https://www.researchgate.net/publication/220365325_Mapping_knowledge_structure_by_keyword_co-occurrence_A_first_look_at_journal_papers_in_Technology_Foresight. [Accessed: 08- Jan- 2020].