Abstract: 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.

Zainab Amjad and Imran Ihsan, “VerbNet based Citation Sentiment Class Assignment using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110973

@article{Amjad2020,
title = {VerbNet based Citation Sentiment Class Assignment using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110973},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110973},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Zainab Amjad and Imran Ihsan}
}