Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysis, which involved a combination of hierarchical clustering and K-means analysis. The hierarchical clustering and LDA decided the number of required categories as seven, and the K-means cluster analysis classified the overall documents into seven categories. This study utilized co-word analysis to check the suitability of the K-means classification, analyzed the terms with high TF-IDF wights for distinct K-means groups, and examined the terms for different topics with the LDA technique. A comparison of the results demonstrated that most categories that were recognized with K-means and LDA methods were the same and shared similar words; however, two categories had slight differences. The involvement of field experts assisted with the consistency and correctness of the classified topics and documents.

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web of Science) database and uses a novel method of topic classification to explore the vocabulary characteristics of the classified articles. The corpora of abstracts are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed to automatically classify the articles; co-word analysis techniques are used to show the characteristics of feature words for distinct clusters, titles, and the consistency of the classified articles. Based on the results for 5783 abstracts, cluster analysis classifies the number of K-means clusters into six categories: travel, culture, sustainability, model, behavior, and hotel. A cross-check method is applied to assess the consistency of the topic classifications, list titles and keywords of the documents with the three smallest distances in each category and apply a strategic diagram to present the features of the distinct categories.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Corporate Social Responsibility (CSR) reporting has become an indispensable mechanism for organizations to communicate their sustainability initiatives. However, the growing volume and complexity of these reports necessitates the integration of Natural Language Processing (NLP)-driven text mining techniques to enhance transparency, comparability, and strategic decision-making. This study employs NLP and text mining methodologies to systematically analyze CSR reports with emphasis on environmental sustainability from major public listed companies in Taiwan. Utilizing Principal Component Analysis (PCA), this study classifies sustainability-related topics, extracts key Sustainable Development Goals (SDG)-aligned terms, and evaluates the textual similarities between CSR reports and SDG targets. Five SDGs encompassing 39 specific targets form the analytical framework, and 225 feature words are identified through text mining. The findings indicate that (1) automated CSR topic classification in Chinese is viable, though expert validation remains crucial for linguistic accuracy and semantic integrity; (2) SDG feature word distribution follows the Pareto Principle, with 10% of words contributing to 50.4%, and 28.4% accounting for 80% of total TF-IDF weights; (3) CSR reporting varies by industry, with financial holdings emphasizing sustainable management, energy supply, and water efficiency, while the electronics sector prioritizes waste reduction, recycling, and product lifecycle management; (4) PCA-based classification effectively aligns CSR reports with SDG targets, with textual similarity analysis proving more accurate than principal component scores. From a strategic business perspective, these findings offer critical insights for corporate leaders seeking to refine their sustainability strategies. NLP-based CSR analysis enables companies to benchmark their Environmental, Social, and Governance (ESG) performance against industry peers, identify sustainability gaps, and align corporate strategies with evolving regulatory landscapes and stakeholder expectations. Financial institutions can leverage these insights to develop sustainable finance mechanisms, while manufacturers can enhance circular economy practices by optimizing resource efficiency and waste management. Moreover, policymakers and investors can utilize NLP-driven text mining techniques to assess corporate sustainability efforts more systematically, ensuring greater accountability and fostering data-driven decision-making in ESG governance.