Good to be Bad? Distinguishing between Positive and Negative Citations in Scientific Impact

Tools with Artificial Intelligence(2011)

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摘要
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.
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关键词
sentiment score,higher precision rate,positive feedback,raw citation,positive opinion,scientific impact,relevance score,rate citation,higher value,higher citation index,negative citations,sentiment analysis,indexing,rank correlation,information retrieval system,mathematical model,citation analysis,cancer,indexation,bibliometrics,information retrieval
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