Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and Citations
Aristotelis Kampatzis, Antonis Sidiropoulos, Konstantinos Diamantaras, Stefanos Ougiaroglou
Abstract
Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation of citations, presenting a detailed description of techniques and methods ranging from lexicon-based approaches to Machine and Deep Learning models. The importance of understanding both the emotion and the intention behind citations is emphasized, reflecting their critical role in scientific communication. In addition, this study presents the challenges faced by researchers (such as complex scientific terminology, multilingualism, and the abstract nature of scientific discourse), highlighting the need for specialized language processing techniques. Finally, future research directions include improving the quality of datasets as well as exploring architectures and models to improve the accuracy of sentiment detection.