Title :
Good to be Bad? Distinguishing between Positive and Negative Citations in Scientific Impact
Author :
Diana C. Cavalcanti;Ricardo B. C. Prudêncio;Shreyasee S. Pradhan;Jatin Y. Shah;Ricardo S. Pietrobon
Author_Institution :
Centro de Inf., Univ. Fed. de Pernambuco, Recife, Brazil
Abstract :
The impact of a publication is often measured by the number of citations it received, this number being taken as a proxy for the relevance of published work. However, a higher citation index does not necessarily mean that a publication necessarily had a positive feedback from citing authors, as a citation can represent a negative criticism. In order to overcome this limitation, we used sentiment analysis to rate citations as positive, neutral or negative. Adjectives are initially extracted from the citations, with the SentiWordNet lexicon being used to rate the degree of positivity and negativity for each adjective. Relevance scores were then computed to rank citations according to the sentiment expressed in the text corresponding to each citation. As expected for accurate information retrieval systems, higher precision rates were observed in the initial points of the curve. The SRC (0.6728) computed using number of raw citations is lower than the SRC (0.7397) observed by the ranking generated using sentiment scores (Table 3). Conclusion: This result indicates that child articles with higher values of relevance score were in general the ones expressing positive opinion about their parents. Therefore, the ranking generated by sentiment scores had an improved accuracy.
Keywords :
"Equations","Mathematical model","Bibliometrics","Cancer","Google","Position measurement","Information retrieval"
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Print_ISBN :
978-1-4577-2068-0
DOI :
10.1109/ICTAI.2011.32