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| Using Behavior and Influence Assessment of Algorithms Based on Full-text Academic Articles |
| Zhang Chengzhi1, 2, 3, Ding Ruiyi1, 3, Wang Yuzhuo1, 3 |
1. Department of Information Management, Nanjing University of Science & Technology, Nanjing 210094;
2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing 210093;
3. Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094 |
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Abstract Data mining algorithms have been widely used in scientific research and practice. Investigating the mentions of data mining algorithms in academic papers and assessing their influence can help researchers comprehensively understand algorithms used in their field and select those that are appropriate based on a given research task. We used full-text of academic articles to conduct an analysis using the behavior of algorithms and evaluating their influence. This paper considers the field of natural language processing and collects the full-text proceedings accumulated by the China National Conference on Computing Linguistics (CCL) from 1993 to 2016 to conduct a comprehensive examination of the top 10 data mining algorithms based on four aspects: frequency of mention, location of mention, motivation for mention, and age distribution. The impacts of the algorithms are evaluated according to these four aspects. The experimental results show that obvious differences exist among the 10 algorithms; the SVM algorithm has the highest influence, while the CART and Apriori algorithms have low influence. This investigation can provide recommendations for researchers, especially novices whose works are related to data-driven research or applications, as well as introduce new ideas toward the assessment of algorithm influence.
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Received: 04 July 2018
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