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. Citation and Co-Citation Analysis. Document Classification, Sentiment Analysis. 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. Tanguy (2009) argues that the multi category classification scheme is needed for Humanities and Social Science (HSS). Author claims that the negative citations are less than 10% in HSS, however, for scientific research, these are not very rare. Scientific research provides quantitative results and thus a critique and rebuttal is viable, as compared to qualitative results in HSS research. With the help of CRC, research scholar can easily classify the type of reading materials, along with other researcher's point of view in citing the same paper. CRC will help the scholar to gather information at a much faster pace than conventional approach of manually looking up and classifying research papers. They can look up for the type (Class) of reading material required namely positive or negative, thereby, minimizing the time to find relevant research for a topic of interest. The goal of CRC is to be used as a resource for assisting researcher scholars.
Butt et al REF classified the collection of cited documents into two classes such as sentiment positive and sentiment negative.
Classification of Research Citations (CRC)
ArXiv
ArXiv
Computer Science