Capturing research literature attitude towards Sustainable Development Goals: an LLM-based topic modeling approach

TL;DR

This paper introduces an automated LLM-based topic modeling pipeline that analyzes research literature to track the evolution of attitudes towards Sustainable Development Goals from 2006 to 2023, enhancing interpretability and reproducibility.

Contribution

It presents a novel LLM-based embedding method and hyperparameter optimization for large-scale topic modeling of scientific abstracts related to SDGs.

Findings

Identified hundreds of topics across thousands of documents

Visualized temporal evolution of SDG-related research topics

Provided a reproducible, scalable workflow for big-text analysis

Abstract

The world is facing a multitude of challenges that hinder the development of human civilization and the well-being of humanity on the planet. The Sustainable Development Goals (SDGs) were formulated by the United Nations in 2015 to address these global challenges by 2030. Natural language processing techniques can help uncover discussions on SDGs within research literature. We propose a completely automated pipeline to 1) fetch content from the Scopus database and prepare datasets dedicated to five groups of SDGs; 2) perform topic modeling, a statistical technique used to identify topics in large collections of textual data; and 3) enable topic exploration through keywords-based search and topic frequency time series extraction. For topic modeling, we leverage the stack of BERTopic scaled up to be applied on large corpora of textual documents (we find hundreds of topics on hundreds of thousands of documents), introducing i) a novel LLM-based embeddings computation for representing scientific abstracts in the continuous space and ii) a hyperparameter optimizer to efficiently find the best configuration for any new big datasets. We additionally produce the visualization of results on interactive dashboards reporting topics' temporal evolution. Results are made inspectable and explorable, contributing to the interpretability of the topic modeling process. Our proposed LLM-based topic modeling pipeline for big-text datasets allows users to capture insights on the evolution of the attitude toward SDGs within scientific abstracts in the 2006-2023 time span. All the results are reproducible by using our system; the workflow can be generalized to be applied at any point in time to any big corpus of textual documents.