Abstract
Citation count of any research paper published is valuable to researcher’s career. Millions of research papers are available and it keeps growing fast. These published articles referred by upcoming research, but not all papers having same impact because of huge depository of research papers, only few publications reach researchers. So it will be interesting to study which articles are getting more citation, so in this paper citation count are considered as quantification parameter. To bring quality literature, it is important to analyze text in the research paper. With this motivation, this paper focuses on different technology to predict citation count considering text and structure features from research articles. For implementation purpose, title, abstract and conclusion fields are abstracted from research papers as the main content to analyze. The linguistic analysis of the corpus of research papers having high and low citation count is done using Natural Language Processing and Machine Learning. A system is implemented using supervised classification model, which takes input few features of a particular publication and gives output as it belongs to either high or low citation category after 9 to 10 years of its publication. There are few classification models considered to evolve learning process and appraise its performance using few performance measures. Experimental results on dataset shows performance accuracy of 60.67% by Random Forest model. The comprehensive experiments on dataset exhibits that proposed models outperform and achieve convincing results.
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Porwal, P., Devare, M.H. (2021). Citation Classification Prediction Implying Text Features Using Natural Language Processing and Supervised Machine Learning Algorithms. In: Santosh, K.C., Gawali, B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-0507-9_46
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DOI: https://doi.org/10.1007/978-981-16-0507-9_46
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