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.

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