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Identifying Citation Sentiment and its Influence while Indexing Scientific Papers

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Title:Identifying Citation Sentiment and its Influence while Indexing Scientific Papers
Authors:Ghosh, Souvick
Shah, Chirag
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
citation sentiment analysis
decision analytics
machine learning
ranking and indexing
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Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/64049
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.307
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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