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 insti...
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IEEE
2022-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9858147/ |
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| author | Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Rimsha Iftikhar Zubair Nawaz Yazeed Yasin Ghadi |
| author_facet | Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Rimsha Iftikhar Zubair Nawaz Yazeed Yasin Ghadi |
| author_sort | Shahzad Nazir |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a08757729b7e48b487a1584b1af523b6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a08757729b7e48b487a1584b1af523b62025-01-30T00:00:58ZengIEEEIEEE Access2169-35362022-01-0110879908800010.1109/ACCESS.2022.31994209858147Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation WeightsShahzad Nazir0Muhammad Asif1https://orcid.org/0000-0003-1839-2527Shahbaz Ahmad2https://orcid.org/0000-0003-0148-4521Hanan Aljuaid3https://orcid.org/0000-0001-6042-0283Rimsha Iftikhar4Zubair Nawaz5https://orcid.org/0000-0002-7989-8401Yazeed Yasin Ghadi6https://orcid.org/0000-0002-7121-495XDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), Riyadh, Saudi ArabiaDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Data Science, University of the Punjab, Lahore, PakistanDepartment of Computer Science/Software Engineering, Al Ain University, Abu Dhabi, United Arab EmiratesA 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.https://ieeexplore.ieee.org/document/9858147/Important citation identificationsentiment analysisweight assignmentmachine learning |
| spellingShingle | Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Rimsha Iftikhar Zubair Nawaz Yazeed Yasin Ghadi Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights IEEE Access Important citation identification sentiment analysis weight assignment machine learning |
| title | Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights |
| title_full | Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights |
| title_fullStr | Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights |
| title_full_unstemmed | Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights |
| title_short | Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights |
| title_sort | important citation identification by exploding the sentiment analysis and section wise in text citation weights |
| topic | Important citation identification sentiment analysis weight assignment machine learning |
| url | https://ieeexplore.ieee.org/document/9858147/ |
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