Mining of Research Topics of Green Consumption in China Based on Text Mining and Complex Network

Subjects: Library Science，Information Science >> Literature Work submitted time 2023-11-07 Cooperative journals: 《文献与数据学报》

Liu Jieping Xu Jinya

Abstract： ［Purpose/significance］A fundamental goal of China’s 14th Five-Year Plan and Vision 2035 is to promote “green consumption”. Identifying research topics is essential because it facilitates staying up-to-date with the latest developments and trends in the field of green consumption, providing indispensable guidance for future studies. ［Method/process］Our proposed method comprehensively considers the literature title, abstract, and keywords using text mining and complex network theories. Our method involves utilizing text word segmentation technology to extract subject headings and employing the Analytic Hierarchy Process(AHP) to determine the co-occurrence weight of two-tuple subject phrases. We provide the TI-g index as a proposal by optimizing the traditional word frequency g index through the inclusion of the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm because the word frequency g index is ineffective at filtering out “high-frequency generic words.” This study focuses on academic literature on green consumption in the period of 2010 to 2022 in China. ［Result/conclusion］A heatmap was generated to display shifts in research topics since 2010, complemented by the identification of recent hotspots of research in this field since 2018.Our analysis identified four major subject fields and highlighted the research limitations present in each.

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