Sentiment Analysis of Scientific Literature for Research Trend and Impact Assessment
Keywords:
sentiment analysis; scientific literature; research impact; science of science; infrastructure governance; fairness; research evaluationAbstract
The accelerating growth of scientific literature has made it impossible for researchers, funders, and institutions to synthesize emerging trends and assess research impact through expert reading alone. Sentiment analysis offers a promising complement to citation-based and altmetric indicators by extracting evaluative language from articles, citation contexts, peer reviews, and related scientific texts. However, applying sentiment analysis to scientific literature is not merely a text classification task. It is a systems problem involving corpus construction, annotation design, model architecture, aggregation, governance, evaluation, and organizational deployment. This paper presents a system-level examination of sentiment analysis for research trend and impact assessment. It discusses the conceptual boundaries of scientific sentiment, the infrastructure needed to acquire and process heterogeneous literature, and the architectural trade-offs between lexicon-based, supervised, and transformer-based classification approaches. It further addresses governance, fairness, robustness, and policy concerns that arise when sentiment signals are embedded in research evaluation workflows. The paper argues that sentiment outputs should be treated as interpretive signals rather than objective quality scores, and that they must be surrounded by transparency, uncertainty reporting, and human oversight. The analysis draws on perspectives from the science of science, natural language processing, bibliometrics, responsible artificial intelligence, and research policy to provide a forward-looking account of how sentiment analysis can support more nuanced and responsible assessments of scientific activity.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
