Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights

A massive research corpus is generated in this epoch based on some previously established concepts or findings. For the acknowledgment of the base knowledge, researchers perform citations. Citations are the key considerations used in finding the different research measures, such as ranking the institutions, researchers, countries, computing the impact factor of journals, allocating research funds, etc. But in calculating these critical measures, citations are treated equally. However, researchers have argued that all citations can never be equally influential. Therefore, researchers have proposed other techniques to identify the important content-based, meta-data-based, and bibliographic-based citations. However, the produced results by the state-of-the-art still need to be improved. In this research work, we proposed an approach based on two primary modules, 1) The section-wise citation count and 2) Sentiment based analysis of citation sentences. The first technique is based on extracting the different sections of the research articles and performing citation count. We applied Neural Network and Multiple Regression on section-wise citations for automatic weight assignment. The citation sentences were extracted in the second approach, and sentiment analysis was used for sentences. Citations were classified with Support Vector Machine, Multilayer Perceptron, and Random Forest. F-measure, Recall, and Precision were considered to evaluate the results, compared with the state-of-the-art results. The value of precision with the proposed approach was enhanced to 0.94.