VerbNet based Citation Sentiment Class Assignment using Machine Learning

Citations are used to establish a link between articles. This intent has changed over the years, and citations are now being used as a criterion for evaluating the research work or the author and has become one of the most important criteria for granting rewards or incentives. As a result, many unethical activities related to the use of citations have emerged. That is why content-based citation sentiment analysis techniques are developed on the hypothesis that all citations are not equal. There are several pieces of research to find the sentiment of a citation, however, only a handful of techniques that have used citation sentences for this purpose. In this research, we have proposed a verb-oriented citation sentiment classification for researchers by semantically analyzing verbs within a citation text using VerbNet Ontology, natural language processing & four different machine learning algorithms. Our proposed methodology emphasizes the verb as a fundamental element of opinion. By developing and assessing the proposed methodology and according to benchmark results, the methodology can perform well while dealing with a variety of datasets. The technique has shown promising results using Support Vector Classifier.