Classification of Research Citations (CRC)
This work addresses the need for automated sentiment analysis in academic literature review, though it is incremental as it applies existing methods to a new task.
The authors tackled the problem of automatically classifying research citations as sentiment positive or negative by extracting cited text and using a sentiment lexicon with a Naïve-Bayes classifier, achieving 80% accuracy on a dataset of 150 computer science papers.
Research is a continuous phenomenon. It is recursive in nature. Every research is based on some earlier research outcome. A general approach in reviewing the literature for a problem is to categorize earlier work for the same problem as positive and negative citations. In this paper, we propose a novel automated technique, which classifies whether an earlier work is cited as sentiment positive or sentiment negative. Our approach first extracted the portion of the cited text from citing paper. Using a sentiment lexicon we classify the citation as positive or negative by picking a window of at most five (5) sentences around the cited place (corpus). We have used Naïve-Bayes Classifier for sentiment analysis. The algorithm is evaluated on a manually annotated and class labelled collection of 150 research papers from the domain of computer science. Our preliminary results show an accuracy of 80%. We assert that our approach can be generalized to classification of scientific research papers in different disciplines.