Themes and Trends in Text Mining Research: Insights from Online News and Annual Reports

This study aims to uncover recent trends and themes in text mining research, focusing on online news and annual report data sources. These sources are rich in content, real-world context, and domain-specific information, making them crucial for text mining. The primary research questions guiding this study are: 1) What are the emerging trends in text mining research applied to online news and annual reports? 2) What are the themes generated based on systematic literature review and text mining technique? To address this question, the study systematically reviews a large number of related studies using the PRISMA review protocol and text mining techniques from the SCOPUS and Web of Science databases. After thorough evaluation, 34 selected articles were analyzed. The PRISMA review protocol ensures transparency and completeness in reporting the review process through its standardized approach for systematic reviews. Additionally, this systematic review explores advancements in text mining techniques such as document clustering and topic modeling, which have facilitated the identification and extraction of relevant evidence from vast amounts of textual data. The study’s findings identified four primary themes (text mining/text analytics, machine learning, deep learning, ensemble methods) with 19 sub-themes related to each theme’s methodology when applying the PRISMA protocol. By utilizing a text mining techniques, five topics were uncovered based on article keywords (text mining, text analytics, machine learning, deep learning, and ensemble) and ten topics emerged based on article abstracts. Underlying both approaches is the consistent recognition of four main areas: text mining/text analytics, machine learning, deep learning, and ensemble methods. This systematic review offers a comprehensive overview of recent text mining research and the emerging trends in this field. It highlights the importance of systematic reviews in synthesizing existing literature and identifying areas for future research.