Research Literature Survey with Text Mining
Researchers conducting searches and reviews of relevant literature face an increasingly complex and voluminous task. In extending the body of relevant knowledge, it has always been important to work hard to gather, organize, analyze, and assimilate existing information from the literature, particularly from one’s home discipline. With the increasing abundance of potentially significant research being reported in related fields, and even in what are traditionally deemed to be nonrelated fields of study, the researcher’s task is ever more daunting, if a thorough job is desired.
In new streams of research, the researcher’s task may be even more tedious and complex. Trying to ferret out relevant work that others have reported may be difficult, at best, and perhaps even near impossible if traditional, largely manual reviews of published literature are required. Even with a legion of dedicated graduate students or helpful colleagues, trying to cover all potentially relevant published work is problematic.
Many scholarly conferences take place every year. In addition to extending the body of knowledge of the current focus of a conference, organizers often desire to offer additional mini-tracks and workshops. In many cases, these additional events are intended to introduce the attendees to significant streams of research in related fields of study and to try to identify the “next big thing” in terms of research interests and focus. Identifying reasonable candidate topics for such mini-tracks and workshops is often subjective rather than derived objectively from the existing and emerging research.
In a recent study, Delen and Crossland (2008) proposed a method to greatly assist and enhance the efforts of the researchers by enabling a semiautomated analysis of large volumes of published literature through the application of text mining. Using standard digital libraries and online publication search engines, the authors downloaded and collected all of the available articles for the three major journals in the field of management information systems: MIS Quarterly (MISQ), Information Systems Research (ISR), and the Journal of Management Information Systems (JMIS). In order to maintain the same time interval for all three journals (for potential comparative longitudinal studies), the journal with the most recent starting date for its digital publication availability was used as the start time for this study (i.e., JMIS articles have been digitally available since 1994). For each article, they extracted the title, abstract, author list, published keywords, volume, issue number, and year of publication. They then loaded all of the article data into a simple database file. Also included in the combined data set was a field that designated the journal type of each article for likely discriminatory analysis. Editorial notes, research notes, and executive overviews were omitted from the collection. Table 5.2 shows how the data was presented in a tabular format.
In the analysis phase, they chose to use only the abstract of an article as the source of information extraction. They chose not to include the keywords listed with the publications for two main reasons: (1) under normal circumstances, the abstract would already include the listed keywords, and therefore inclusion of the listed keywords for the analysis would mean repeating the same information and potentially giving them unmerited weight; and (2) the listed keywords may be terms that authors would like their article to be associated with (as opposed to what is really contained in the article), therefore potentially introducing unquantifiable bias to the analysis of the content.
The first exploratory study was to look at the longitudinal perspective of the three journals (i.e., evolution of research topics over time). In order to conduct a longitudinal study, they divided the 12-year period (from 1994 to 2005) into four 3-year periods for each of the three journals. This framework led to 12 text mining experiments with 12 mutually exclusive data sets. At this point, for each of the 12 data sets they used text mining to
extract the most descriptive terms from these collections of articles represented by their abstracts. The results were tabulated and examined for time-varying changes in the terms published in these three journals.
As a second exploration, using the complete data set (including all three journals and all four periods), they conducted a clustering analysis. Clustering is arguably the most commonly used text mining technique. Clustering was used in this study to identify the