Text Mining Approach for Trend Tracking in Scientific Research: A Case Study on Forest Fire

Abstract

Scientific studies are increasing day by day with the development of technology. Today, more than 171 billion academic records are made available to researchers via the Web of Science database, which is frequently followed by the scientific community, and is where records of articles, proceedings, and books in many different fields are kept. More than 40 thousand studies are reached when a search is made for research on forest fires in the relevant database. It is unfeasible to examine and read so many publications and understand what topics are important in the relevant field, what is trending, or whether there is a difference between the subjects studied based on years and/or regions/countries. The most effective and scientific method of deriving information from such large and unstructured data is text mining. In this study, text mining is used to reveal where the research on forest fires in the Web of Science database concentrates, which study topics have emerged, how an issue’s level of importance changes over the years, and which topics different countries focus on. Therefore, the abstracts of approximately 32 thousand articles published in English were collected and analyzed based on the country of the authors and the published years. Over 600 words in the abstracts were indexed for each article and their importance was calculated according to inverse document frequency. A size reduction was made to determine the main concepts of the articles by using the singular value decomposition and a total of 29 different concepts were found. Among these, important concepts can be mentioned such as damage to vegetation and species affected, post-fire actions, fire management, and post-fire structural changes. Considering all the articles, studies on soil, fuel (biofuel), treatment, emissions, and species were found to be important. The results we have obtained in this study are by no means a summary of the research carried out in the field; they do, however, allow statistical due diligence concerning, for example, which subjects are important in the relevant field, the determination of increasing and decreasing trending topics, which countries attach importance to in the same research, and so on. Thus, it will function as be a guide in terms of the direction, timing, and budget allocation of research plans in a specific area in the future.

Using a hybrid methodology for literature review: a case study in depression research

“…Usai et al (2018) conducted a systematic review using a text mining technique at the term level, revealing the emergence of diverse research fields such as business, marketing and finance from 2010 to 2017 due to a greater interest in the online environment. Eroglu (2023) used text mining to analyze approximately 32,000 articles on forest fires published in English, revealing various important concepts and trends, thus serving as a guide for future research plans.…”

“…Our analysis of these studies has enriched our own methodological approach and underlined the value of thorough and creative literature review methods in our discipline. The work of these researchers attests to the immense potential of text mining as a tool to pinpoint pertinent literature, decrease the burdensomeness of systematic reviews and accentuate emerging trends in any particular research domain (Eroglu, 2023; O’Mara-Eves et al , 2015; Thomas et al , 2011; Usai et al , 2018).…”

Text Mining for News Forecasting on The Turnback Hoax Website

“…Text mining is one of the specialized subfields of data mining [36]. Text mining is a new technique for semiautomatically identifying useful information from unstructured text data by indexing the text's words [37]. Text mining is a technique that is frequently used in literary studies to organize textual data and identify correlations and latent semantic patterns in the data [38].…”

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

“…It uses words indexing methods to exploit the enormous amount of information available in text documents [17]. Many textmining techniques established in the literature are frequently used for information discovery in both academia and industry [18].…”