Identifying Citation Sentiment and its Influence while Indexing Scientific Papers

dc.contributor.author Ghosh, Souvick
dc.contributor.author Shah, Chirag
dc.date.accessioned 2020-01-04T07:40:19Z
dc.date.available 2020-01-04T07:40:19Z
dc.date.issued 2020-01-07
dc.description.abstract Sentiment analysis has proven to be a popular research area for analyzing social media texts, newspaper articles, and product reviews. However, sentiment analysis of citation instances is a relatively unexplored area of research. For scientific papers, it is often assumed that the sentiment associated with citation instances is inherently positive. This assumption is due to the hedged nature of sentiment in citations, which is difficult to identify and classify. As a result, most of the existing indexes focus only on the frequency of citation. In this paper, we highlight the importance of considering the sentiment of citation while preparing ranking indexes for scientific literature. We perform automatic sentiment classification of citation instances on the ACL Anthology collection of papers. Next, we use the sentiment score in addition to the frequency of citation to build a ranking index for this collection of scientific papers. By using various baselines, we highlight the impact of our index on the ACL Anthology collection of papers. Our research contributes toward building more sentiment sensitive ranking index which better underlines the influence and usefulness of research papers.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.307
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64049
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject citation sentiment analysis
dc.subject decision analytics
dc.subject machine learning
dc.subject ranking and indexing
dc.subject scientometrics
dc.title Identifying Citation Sentiment and its Influence while Indexing Scientific Papers
dc.type Conference Paper
dc.type.dcmi Text
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