Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model
Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/6/3203 |
| _version_ | 1797472881929617408 |
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| author | Musarat Karim Malik Muhammad Saad Missen Muhammad Umer Saima Sadiq Abdullah Mohamed Imran Ashraf |
| author_facet | Musarat Karim Malik Muhammad Saad Missen Muhammad Umer Saima Sadiq Abdullah Mohamed Imran Ashraf |
| author_sort | Musarat Karim |
| collection | DOAJ |
| description | Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of authors and journals. However, for a fair evaluation, the qualitative aspect should be considered along with the quantitative measures. The sentiments expressed in citation play an important role in evaluating the quality of the research because the citation may be used to indicate appreciation, criticism, or a basis for carrying on research. In-text citation analysis is a challenging task, despite the use of machine learning models and automatic sentiment annotation. Additionally, the use of deep learning models and word embedding is not studied very well. This study performs several experiments with machine learning and deep learning models using fastText, fastText subword, global vectors, and their blending for word representation to perform in-text sentiment analysis. A dimensionality reduction technique called principal component analysis (PCA) is utilized to reduce the feature vectors before passing them to the classifier. Additionally, a customized convolutional neural network (CNN) is presented to obtain higher classification accuracy. Results suggest that the deep learning CNN coupled with fastText word embedding produces the best results in terms of accuracy, precision, recall, and F1 measure. |
| first_indexed | 2024-03-09T20:07:28Z |
| format | Article |
| id | doaj.art-20de7e112c22448781814fe708f1fb5c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:07:28Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-20de7e112c22448781814fe708f1fb5c2023-11-24T00:25:28ZengMDPI AGApplied Sciences2076-34172022-03-01126320310.3390/app12063203Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network ModelMusarat Karim0Malik Muhammad Saad Missen1Muhammad Umer2Saima Sadiq3Abdullah Mohamed4Imran Ashraf5Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanResearch Centre, Future University in Egypt, New Cairo 11745, EgyptInformation and Communication Engineering, Yeungnam University, Gyeongsan 38541, KoreaCitation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of authors and journals. However, for a fair evaluation, the qualitative aspect should be considered along with the quantitative measures. The sentiments expressed in citation play an important role in evaluating the quality of the research because the citation may be used to indicate appreciation, criticism, or a basis for carrying on research. In-text citation analysis is a challenging task, despite the use of machine learning models and automatic sentiment annotation. Additionally, the use of deep learning models and word embedding is not studied very well. This study performs several experiments with machine learning and deep learning models using fastText, fastText subword, global vectors, and their blending for word representation to perform in-text sentiment analysis. A dimensionality reduction technique called principal component analysis (PCA) is utilized to reduce the feature vectors before passing them to the classifier. Additionally, a customized convolutional neural network (CNN) is presented to obtain higher classification accuracy. Results suggest that the deep learning CNN coupled with fastText word embedding produces the best results in terms of accuracy, precision, recall, and F1 measure.https://www.mdpi.com/2076-3417/12/6/3203in-text citationcitation context analysisdeep learningconvolutional neural networkword embedding |
| spellingShingle | Musarat Karim Malik Muhammad Saad Missen Muhammad Umer Saima Sadiq Abdullah Mohamed Imran Ashraf Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model Applied Sciences in-text citation citation context analysis deep learning convolutional neural network word embedding |
| title | Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model |
| title_full | Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model |
| title_fullStr | Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model |
| title_full_unstemmed | Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model |
| title_short | Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model |
| title_sort | citation context analysis using combined feature embedding and deep convolutional neural network model |
| topic | in-text citation citation context analysis deep learning convolutional neural network word embedding |
| url | https://www.mdpi.com/2076-3417/12/6/3203 |
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