WordCommentsAnalyzer: A windows software tool for qualitative research

Introduction

Commercial qualitative data analysis (QDA) software tools such as NVivo and Atlas.ti seem to be the most popular in the qualitative research community¹. However, learning to use these complex software tools may be inconvenient for some researchers. Moreover, the purchase of commercial QDA software may not be affordable for some researchers. On the other hand, free or open-source solutions that are available often do not provide a smooth editing and markup experience (e.g., QDA Miner Lite does not support Persian and Arabic languages; CATMA and CAT² are not fast due to their web-based nature). For these reasons, some researchers use professional word processing programs for their qualitative research projects.

The use of Microsoft Word for QDA is commonly documented^3,4. Using Word comments provides a straightforward way to annotate specific portions of the text and attach keywords or categories (codes) to them. However, as the amount of data grows, organizing codes in Word comments becomes an exhausting task.

In this article, we present WordCommentsAnalyzer, a free, open-source tool that makes it possible for qualitative researchers to automate organization of the qualitative codes through a fast and easy-to-learn user interface while coding the textual material using Microsoft Word as a professional, familiar word procesing software.

Methods

Implementation

This software is written in C# programming language using .NET Framework 4.5.2. The software also makes use of OpenXml library to extract comments from Word documents. Recent versions of Word store documents in XML format. OpenXml provides an easy way to query comments from a document. To facilitate assigning multiple codes to a piece of text, we assume a simple convention: different codes are entered in a comment with line breaks between them (as the descendant paragraphs of the comment element). The software uses a relational model approach to store the extracted codes and uses language integrated queries to collect different text portions related to each code, to calculate the code frequencies and to sort the codes by frequency. The visual interface of the program consists of three side-by-side panels (Figure 1). The left panel shows the codes in the comments with their counts, the middle one provides a code tree that the user can intuitively organize their codes in and the right panel shows the data extracts pertaining to each code. In the left panel, the code list can be filtered to find specific codes. The user can place codes in the code hierarchy simply by using drag-and-drop. The tree also enables the user to move codes in the hierarchy if needed. The user can introduce a new parent code or a code that is of a higher level of abstraction. Additionally, the codes are changed or combined by being wrapped in new codes. The code hierarchy tree is saved as a tab-indented text file in the data folder (codehierarchy.txt). The tree is auto-saved every minute and can also be manually saved by clicking a save button in the interface. The previous tree files are backed up in a subfolder of the data folder.

Figure 1. An illustration of the three side-by-side panels of the WordCommentsAnalyzer user interface.

The left panel shows the codes in the comments with their counts, the middle panel provides a code tree for intuitive organization of the codes and the right panel shows the data extracts pertaining to each code (or to children of a parent code). The code list in the left panel can be filtered to find specific codes. The user can place codes in the code hierarchy simply by using drag-and-drop. The tree also enables the user to move codes in the hierarchy if needed. The user can introduce a new parent code. The codes are changed or combined by being wrapped in new codes.

Use case

To illustrate how to use the software, we present a mini-study of Twitter’s Tweets from 17 January 2017 to 10 April 2018. The Tweets with the #successfulaging hashtag were copied into two Word documents based on the year in which the Tweets were posted (Supplementary File 1). We reviewed the Tweets and added comments (line-break-separated codes) to portions of texts containing interesting notions related to successful aging. Two examples of these text portions are reproduced in Figure 2.

Figure 2. Two text samples of #successfulaging Tweets, which are commented using line-break-separated codes.

The codes describe notable topics concerning the text samples.

After adding comments to Word documents, we run WordCommentsAnalyzer, select the folder containing the Word documents and click the Analyze button. The program analyzes the comments and shows a list of codes with their counts in the left panel. The middle panel enables us to organize the codes by placing them in a code hierarchy (Figure 3). For example, we can find a number of codes related to health by filtering the code list by the word of “health”. Then we add the code of “Health”, which is a parent code, to the hierarchy by dragging and dropping it onto the root node of “Code Hierarchy”. The codes of “Brain health”, “Physical health”, and “Health care” can then be drag-and-dropped onto the node of “Health”. Likewise, “Oral health” is inserted into “Physical health”. When organizing the codes, we could check the right panel to assure the data extracts support the codes. Also, the codes inserted into the hierarchy will be highlighted in the code list to help keep track of the organized codes.

Figure 3. Features in the left and middle panels of the WordCommentsAnalyzer user interface.

The user can find specific codes by filtering the code list (e.g., by the word of “health”) and organize the codes (from the left panel) by dragging and dropping them into the code hierarchy tree (the right panel).

Figure 4 presents a formatted version of codehierarchy.txt (Supplementary File 2) when we organized the Tweet codes with at least two counts. As shown in this figure, the themes of health, retirement, happiness and being active represent the richest themes in the Tweets of #successful aging.

Figure 4. A formatted version of tab-indented text output file of the code hierarchy tree.

When we organized the Tweet codes with at least two counts. The large branches of the code tree can help the researcher identify the richest themes in the data. Thus, themes of health, retirement, happiness, and being active are probably the major themes in the Tweets with the hashtag #successfulaging.

Conclusion

This article presents a Windows software tool for organizing comments in Word documents. WordCommentsAnalyzer facilitates organizing codes in a code hierarchy for qualitative researchers who are interested in using Word documents to annotate their data.

Software availability

Source code available from: https://github.com/ehsabd/word-comments-analyzer.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.1228604⁵.

License: GNU General Public License 3.0.