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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉鋼(Kang-Ernest Liu) | |
| dc.contributor.author | Ting Hsiung | en |
| dc.contributor.author | 熊庭 | zh_TW |
| dc.date.accessioned | 2021-06-15T13:40:33Z | - |
| dc.date.available | 2024-08-29 | |
| dc.date.copyright | 2020-08-21 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-11 | |
| dc.identifier.citation | Abrishami, A., Aliakbary, S., 2019. “Predicting citation counts based on deep neural network learning techniques,” Journal of Informetrics,13(2): 485-499. Abramo, G., D’Angelo, C.A., 2016. “Refrain from adopting the combination of citation and journal metrics to grade publications, as used in the Italian national research assessment exercise (VQR 2011–2014),” Scientometrics, 109(3): 2053-2065. Adams, J., 2005. “Early citation counts correlate with accumulated impact,” Scientometrics. 63(3): 567-581. Aksnes, D.W., Taxt, R.E., 2004. “Peer reviews and bibliometric indicators: a comparative study at a Norwegian university,” Research Evaluation. 13: 33-41. Aksnes, D.W., 2003. “Characteristics of highly cited paper,” Research Evaluation. 12(3): 159-170. Allahyari, M., Pouriyeh, S., Assefi, M., Safae, I.S., Trippe, E.D., Gutierrez, J.B. Kochut, K., 2017. “A brief survey of text mining: classification, clustering and extraction techniques,” arXiv:1707.02919 (arXiv preprint). Alper, K.U., Serkan, G., 2014. “The impact of preprocessing on text classification,” Information Processing Management. 50(1): 104-112. Baba, Takahiro, Kensuke Baba, Daisuke Ikeda., 2019. “Citation Count Prediction using Abstracts,” Journal of Web Engineering. 18(1): 207-228. Blei, D.M., Ng, A.Y., Jordan, M.I., 2003. “Latent Dirichlet allocation,” Journal of Machine Learning Research. 3: 993-1022. Blei, D.M., 2011. “Probabilistic topic models,” Communications of the ACM. 55(4):77-84. Bornmann, L. Daniel, H.D., 2005. “Selection of research fellowship recipients by committee peer review. Analysis of reliability, fairness and predictive validity of Board of Trustees' decisions,” Scientometrics. 63:2 97-320. Bornmann, L., Schier, H., Marx, W., Daniel, H.D., 2012. “What factors determine citation counts of publications in chemistry besides their quality?” Journal of Informetrics. 6: 11-18. Bornmann, L., 2014. “How are excellent (highly cited) papers defined in bibliometrics? A quantitative analysis of the literature,” Research Evaluation. 23: 166-173. Brooks, T.A., 1986. “Evidence of complies citer motivations.” Journal of the American Society for Information Science.37(1): 34-36. Buela-Casal, G., Zych, I., 2010. “Analysis of the relationship between the number of citations and the quality evaluated by experts in psychology journals.” Psicothema. 22(2): 270-276. Brody T., Harnad S., Carr L., 2006. “Earlier Web Usage Statistics as Predictors of Later Citation Impact.” Journal of the American Association for Information Science and Technology. 57(8): 1060-1072. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002. “SMOTE: Synthetic Minority Over-Sampling Technique.” Journal of Artificial Intelligence Research. 16: 321-357. Chen, T., Guestrin, C., 2016. “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. Pages: 785-794. Collet, F., Robertson, D.A., Lup, D., 2014. “When does brokerage matter? Citation impact of research teams in an emerging academic field,” Strategic Organization.12(3): 157-179. Cortes, C., Vapnik, V., 1995. “Support-vector networks,” Machine Learning. 20: 273-297. Cunningham P., Delany SJ., 2007. “k-nearest neighbour classifiers.” Mult Classif Syst.34(8): 1-17. Dervos, D.A., Kalkanis, T, 2005. “cc-IFF: A Cascading Citations Impact Factor Framework for the Automatic Ranking of Research Publications,” IEEE International Workshop on Data Acquisition and Advanced Computing Systems Workshop Proceedings. Pages: 668-673. Erdt, M., Nagarajan, A., Sin, S.C.J., Theng, Y.L, 2016. “Altmetrics: An analysis of the state-of-the-art in measuring research impact on social media,” Scientometrics.109(2): 1117-1166. Fang, D., Yang, H., Gao, B., Li, X., 2018. “Discovering research topics from library electronic references using latent Dirichlet allocation,” Library Hi Tech.36(3): 400-410. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. “Knowledge Discovery and Data Mining: Towards a Unifying Framework,” Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, Oregon. Pages: 82-88. Finch, T., O’Hanlon, N., Dudley, S.P., 2018. “Tweeting birds: Online mentions predict future citations in ornithology,” Royal Society Open Science.4(11). DOI: ARTN 171371/rsos.171371 Fu, L.D., Aliferis, C.F., 2010. “Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature,” Scientometrics.85(1): 257-270. Gross, P.L. Gross, E.M., 1927. “College libraries and chemical education. Science,” Science.66(1713): 385-9. González-Pereira, B., Guerrero-Bote, V.P., Moya-Anegón, F., 2010. “A new approach to the metric of journals’ scientific prestige: the SJR indicator,” Journal of Infometrics. 4: 379-391. Habibzadeh, F., Yadollahie, M., 2010. “Are shorter article titles more attractive for citations? Cross-sectional study of 22 scientific journals,” Croatian Medical Journal. 51(2): 165-170. Haddi, E., Liu, X., Shi, Y., 2013. “The Role of Text Pre-processing in Sentiment Analysis,” Procedia Computer Science.17: 26-32. He, H., Garcia, EA., 2009. “Learning from imbalanced data,” IEEE Trans Knowl Data Eng. 21(9): 1263-1284. Hirsch, J.E., 2005. “An index to quantify an individual’s scientific research output,” Proceedings of the National Academy of Sciences.102(46): 16569-16572. Ho, T.K., 1995. “Random decision forests,” Third International Conference on Document Analysis and Recognition, Montreal, Canada, August.14-16: 278-282. Hu, Y.H., Tai, C.T., Liu, K.E., Cai, C.F., 2020. “Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity,” Journal of Informetrics. 14(1): 101004. Inhaber, H., Przednowek, K., 1976. “Quality of research and Nobel Prizes,” Social Studies of Science.6: 33-50. Jiang, J., He, D., Ni, C., 2013. “The correlations between article citation and references’ impact measures: What can we learn,” Proceedings of the ASIST Annual Meeting. 50(1): 1-4. Kohavi, R., 1995. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Pages: 1137-1143. Kossmeier, M., Heinze, G., 2019. “Predicting future citation counts of scientific manuscripts submitted for publication: a cohort study in transplantology.” Transpl Int. 32(6). Kosteas, V., 2018. “Predicting long-run citation counts for articles in top economics journals,” Scientometrics. 115(3): 1395-1412. Kostoff, R.N. 2007. “The difference between highly and poorly cited medical articles in the journal Lancet.” Scientometrics. 72(3): 513-520. Laband, D.N. 2013. “On the use and abuse of economics journal rankings,” The Economic Journal. 123(570): 223-254. Lamb C.T., Gilbert S.L., Ford A.T., 2018. “Tweet success? Scientific communication correlates with increased citations in Ecology and Conservation,” PeerJ.6: e4564. Leimu, R., Koricheva, J., 2005. “What determines the citation frequency of ecological papers?” Trends in Ecology Evolution. 20(1): 28-32. Levitt, J. M., Thelwall, M., 2011. “A combined bibliometric indicator to predict article impact,” Information Processing and Management. 47(2): 300-308. Li, S., Tang, B., He, H., 2016. “An imbalanced learning based MDRTB early warning system,” Journal of Medical Systems. 40(7):1-9. Livne, A., Adar, E., Teevan, J., Dumais, S., 2013. “Predicting citation counts using text and graph mining,” In: Proceedings of the iConference 2013 workshop on computational scientometrics: theory and applications. Lucas, A., Barranco, R., Refa, N., 2019. “EV idle time estimation on charging infrastructure comparing supervised machine learning regressions,” Energies. 12(2): 269. Murtagh, F., 1991. “Multilayer perceptrons for classification and regression,” Neurocomputing. 2(5-6): 183-197. Nielsen, F. Å., 2007. “Scientific citations in Wikipedia,” First Monday. 12(8). Ortega, J. L., 2015. “Relationship between altmetric and bibliometric indicators across academic social sites: the case of CSIC's members,” Journal of Informetrics. 9(4): 39-49. Paiva, CE., da Silveira Nogueira Lima, JP., Paiva, BS., 2012. “Articles with short titles describing the results are cited more often,” Clinics （Sao Paulo）. 67(5): 509-513. Pal, M., Mather, P.M., 2003. “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sensing of Environment. 86(4): 554-565. Piwowar, H., 2013. “Introduction altmetrics: What, why and where?” Bulletin of the American Society for Information Science and Technology. 39(4): 8-9. Piwowar, H., Priem, J., 2013. “The power of altmetrics on a CV.” Bulletin of the American Society for Information Science and Technology. 39(4): 10-13. Priem, J., Hemminger, B.H., 2010. “Scientometrics 2.0: Toward new metrics of scholarly impact on the social web,” First Monday. 15(7) Retrieved from https://firstmonday.org/article/view/2874/2570 Priem, J., Taraborelli, D., Groth, P., Neylon, C., 2011. “Altmetrics: A manifesto,” Retrieved from http://altmetrics.org/manifesto/ Ren, X., Guo, H., Li, S., Wang, S., Li, J., 2017. “A novel image classification method with CNN-XGBoost model,” International Workshop on Digital Watermarking. 10431: 378-390. Rish, I., 2001. “An Empirical Study of the Naive Bayes Classifier.” IJCAI 2001 Workshop on Empirical Methods in AI. Ronald, Rousseau Fred, Y.Ye., 2013. “A multi-metric approach for research evaluation.” Chinese Science Bulletin. 58(26): 3288-3290. Saif, H., Fernandez, M., He, Y., Alani, H., 2014. “On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter.” In: Proc. 9th Language Resources and Evaluation Conference (LREC), Reykjavik, Iceland. Sathianathen, N. J., Lane 3rd. R, Condon, B., Murphy, D. G., Lawrentschuk, N., Weight, C.J. Lamb, A.D., 2019. “Early Online Attention Can Predict Citation Counts for Urological Publications: The #UroSoMe_Score,” European Urology Focus. DOI: https://doi.org/10.1016/j.euf.2019.10.015. Smith, S.D., 2004. “Is an article in a top journal a top article?” Financial Management,4. 33(4): 133-149. Sohrabi, B., Iraj, H., 2017. “The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts.” Scientometrics. 110(1): 1-9. Sokolova, M., Lapalme, G., 2009. “A systematic analysis of performance measures for classification tasks.” Inform Process Manag. 45(5): 427-437. Spasic, I., Ananiadou, S., McNaught, J. Kumar, A. 2005. “Text mining and ontologies in biomedicine: making sense of raw text.” Brief Bioinform. 6: 239-251. Tahamtan, I., Afshar, A.S., Ahamdzadeh, K., 2016 “Factors affecting number of citations: A comprehensive review of the literature.” Scientometrics. 107:1195-1225. Tan, M., Tan, L., Dara, S., Mayuex, C., 2015 “Online defect prediction for imbalanced data.” in Int’l Conf. on Softw -Volume 2, IEEE Press, Pages: 99-108. Thelwall, M., Nevill, T., 2018. “Could scientists use Altmetric. com scores to predict longer-term citation counts? ” Journal of Informetrics. 12(1): 237-248. Tian, Y., Yu, G., Li, P.Y., Wang, L., 2014. “Citation impact prediction for scientific papers using stepwise regression analysis.” Scientometrics. 101(2): 1233-1252. Tseng, Y.H., Lin, C.J. Lin, Y.I., 2007. “Text mining techniques for patent analysis.” Inf Process Manag.43(5): 1216-1247. Van, R., 2004. “Sleeping beauties in science,” Scientometrics. 59(3): 467-472. Waltman, L., Costas, R., 2014. “F1000 recommendations as a potential new data source for research evaluation: A comparison with citations,” Journal of the Association for Information Science and Technology. 65(3): 433-445. Wang, M., Wang, Z., Chen, G., 2019. “Which can better predict the future success of articles? Bibliometric indices or alternative metrics,” Scientometrics. 119(3): 1575-1595. Wang, F., Fan, Y., Zeng, A., Di, Z., 2019. “Can we predict ESI highly cited publications?” Scientometrics. 118(1): 109-125. Wang, M., Yu, G., Xu, J., He, H., Yu, D., An, S., 2012. “Development a case-based classifier for predicting highly cited papers.” Journal of Informetrics. 6(4): 586-599. Wold, Esbensen, Geladi, 1987. “Principal component analysis,” Chemometrics and Intelligent Laboratory Systems. 2(1–3): 37-52. Yan, R., Tang, J., Liu, X., Shan, D., Li, X., 2011. “Citation count prediction: learning to estimate future citations for literature.” In: Proceeding CIKM, Pages: 1247-1252. Yu, T., Yu, G., Li, P.Y., Wang, L., 2014. “Citation impact prediction for scientific papers using stepwise regression analysis.” Scientometrics. 101: 1233-1252. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51598 | - |
| dc.description.abstract | 如何以客觀且效率的方式評斷一篇論文的品質與影響力，一直是學術界廣為討論以及研究的重要議題，透過預測論文的引用次數，我們可以評估論文作者在學術上的未來影響力，幫助研究人員找到具有影響力的文章，並且有效理解研究議題。 隨著網路蓬勃發展，人們開始使用網路媒介交流科學研究，若只使用傳統文獻計量來預測學術影響力，將可能忽略網路媒介所帶來的影響，進而產生偏差。故本研究使用傳統文獻計量指標：引用特徵、期刊特徵、作者特徵、論文特徵四大特徵，以及科學出版物在各種網路平台（新聞、部落格和社交媒體等）上獲得關注的量化指標Altmetrics與期刊文章關鍵字熱門程度等非傳統文獻計量指標來預測期刊論文的未來影響力。旨在建立傳統文獻計量指標與非傳統文獻計量指標來預測高引用期刊文章，最後使用六種機器學習分類模型：K-近鄰演算法、支援向量機、樸素貝氏、隨機森林、神經網路、極限梯度提升，找出最佳分類模型。 本研究實證發現，行銷以及旅遊領域之期刊文章在加入非傳統文獻計量指標後，皆會提升預測性能，且六種分類模型中，xgboost得到最佳的高引用文章的識別能力。基於以上實證結果，能夠幫助研究人更精準地識別出未來具有高影響力的文章。 | zh_TW |
| dc.description.abstract | How to judge the quality and influence of a paper in an objective and efficient way has always been an important topic in academia. We can evaluate the future influence of the paper by predicting citations. It can help researchers find influential articles and understand effectively of the research topics. As the Internet flourishes, people communicate scientific research with internet. If only predict the Impact of Academic Journal Article through traditional bibliometrics indices, it may be biased by ignoring the influence of the Internet.Therefore, this study uses traditional bibliometric Indices: Citation characteristics, Journal characteristics, Author characteristics, and Paper characteristics; non-traditional bibliometric such as Altmetrics that’s the quantitative indicators of scientific publications gain attention on various online platforms (news, blogs, and social media, etc.) and Keyword popularity to predict the influence of journal articles. The main purpose of the thesis is to predict the highly cited journal articles through a Combination of Traditional and Non-Traditional Bibliometric Indices. Using six machine learning classification models: K-nearest neighbor algorithm, support vector machine, naive Bayesian, random forest, neural network, xgboost to find the best classification model. The empirical results show that the addition of non-traditional bibliometric indicators to journal articles in marketing and tourism fields will improve prediction performance. Among the six classification models, xgboost has the best ability to identify highly cited articles. Based on the above empirical results, it can help researchers to more accurately identify high-impact articles. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T13:40:33Z (GMT). No. of bitstreams: 1 U0001-0908202021374700.pdf: 5582067 bytes, checksum: 09290125823dfb2bca4dc4d92c2b64e9 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員審定書 i 致謝 ii 摘要 iii Abstract iv 第1章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 5 1.4 研究問題 5 1.5 預期研究貢獻 6 第2章 文獻探討 7 2.1 引用動機 7 2.2 引用次數的分類 7 2.3 傳統文獻計量指標 10 2.3.1 引用特徵 10 2.3.2 期刊特徵 10 2.3.3 作者特徵 11 2.3.4 論文特徵 12 2.4 非傳統文獻計量指標 13 2.4.1 Altmetrics 13 2.4.2 關鍵字熱門程度 17 第3章 研究方法 26 3.1 資料蒐集與蒐資料前處理 27 3.1.1 傳統文獻計量指標之資料來源與擷取 28 3.1.2 Altmetrics指標資料來源 29 3.1.3 關鍵字熱門程度資料來源 29 3.2 預測分析之變數定義 30 3.2.1 傳統文獻計量指標 30 3.2.2 非傳統傳統文獻計量指標 34 3.2.3 引用次數的分類 41 3.3 建立測模型 42 3.3.1 分類模型 43 3.4 評估預測模型準則 48 3.4.1 交叉驗證法 48 3.4.2 分類模型性能評估 48 3.4.3 處理樣本不平衡問題 50 第4章 實證結果分析 52 4.1 資料說明 52 4.2 敘述統計 56 4.3 樣本分類 58 4.4 實驗結果與分析 62 4.4.1 完整資料集 63 4.4.2 刪減資料集 74 4.4.3 綜合討論 86 第5章 結論與建議 93 5.1 結論 93 5.2 研究限制與未來研究方向 94 參考文獻 95 | |
| dc.language.iso | zh-TW | |
| dc.title | 利用傳統和非傳統文獻計量指標的組合，預測學術期刊論文影響力 | zh_TW |
| dc.title | Predicting the Impact of Academic Journal Article through a Combination of Traditional and Non-Traditional Bibliometric Indices | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張焯然(Jow-Ran Chang),胡雅涵(Ya-Han Hu) | |
| dc.subject.keyword | 替代性計量,關鍵字熱門程度,預測引用,機器學習,隱含狄利克雷分布, | zh_TW |
| dc.subject.keyword | Altmetric,Keyword Popularity,Predictive citation,Machine learning,Latent Dirichlet Allocation, | en |
| dc.relation.page | 100 | |
| dc.identifier.doi | 10.6342/NTU202002730 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-11 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農業經濟學研究所 | zh_TW |
| 顯示於系所單位： | 農業經濟學系 | |
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