A Heuristic Text Analytic Approach for Classifying Research Articles
Affiliation(s)
1 Integrated Information Technology Department, University of South Carolina, Columbia, USA. 2 The Business School, University of Colorado Denver, Denver, USA.
1 Integrated Information Technology Department, University of South Carolina, Columbia, USA. 2 The Business School, University of Colorado Denver, Denver, USA.
ABSTRACT
Classification of research articles is fundamental to analyze and understand research literature. Underlying concepts from both text analytics and concept mining form a foundation for the development of a quantitative heuristic methodology, the Scale of Theoretical and Applied Research (STAR), for classifying research. STAR demonstrates how concept mining may be used to classify research with respect to its theoretical and applied emphases. This research reports on evaluating the STAR heuristic classifier using the Business Analytics domain, by classifying 774 Business Analytics articles from 23 journals. The results indicate that STAR effectively evaluates overall article content of journals to be consistent with the expert opinion of journal editors with regard to the research type disposition of the respective journals.
Classification of research articles is fundamental to analyze and understand research literature. Underlying concepts from both text analytics and concept mining form a foundation for the development of a quantitative heuristic methodology, the Scale of Theoretical and Applied Research (STAR), for classifying research. STAR demonstrates how concept mining may be used to classify research with respect to its theoretical and applied emphases. This research reports on evaluating the STAR heuristic classifier using the Business Analytics domain, by classifying 774 Business Analytics articles from 23 journals. The results indicate that STAR effectively evaluates overall article content of journals to be consistent with the expert opinion of journal editors with regard to the research type disposition of the respective journals.
KEYWORDS
Bibliometric, Business Analytics, Concept Mining, Heuristic, Research Continuum, Text Analytics, Text Mining
Bibliometric, Business Analytics, Concept Mining, Heuristic, Research Continuum, Text Analytics, Text Mining
Cite this paper
Walczak, S. and Kellogg, D. (2015) A Heuristic Text Analytic Approach for Classifying Research Articles. Intelligent Information Management, 7, 7-21. doi: 10.4236/iim.2015.71002.
Walczak, S. and Kellogg, D. (2015) A Heuristic Text Analytic Approach for Classifying Research Articles. Intelligent Information Management, 7, 7-21. doi: 10.4236/iim.2015.71002.
References
[1] Stringer, M.J., Sales-Pardo, M. and Amaral, L.A.N. (2008) Effectiveness of Journal Ranking Schemes as a Tool for Locating Information. PLoS ONE, 3, 1-8. [2] García-Crespo, á., Gómez-Berbís, J.M., Colomo-Palacios, R. and García-Sánchez, F. (2011) Digital Libraries and Web 3.0. The Callimachus DL Approach. Computers in Human Behavior, 27, 1424-1430.
http://dx.doi.org/10.1016/j.chb.2010.07.046 [3] Lee, J.Y., Kim, H. and Kim, P.J. (2010) Domain Analysis with Text Mining: Analysis of Digital Library Research Trends Using Profiling Methods. Journal of Information Science, 36, 144-161.
http://dx.doi.org/10.1177/0165551509353251 [4] Ma, J., Xu, W., Sun, Y., Turban, E., Wang, S. and Liu, O. (2012) An Ontology-Based Text Analytics Method to Cluster Proposals for Research Project Selection. Transactions on Systems, Man, and Cybernetics—Part A, 42, 784-790.http://dx.doi.org/10.1109/TSMCA.2011.2172205 [5] Hjørland, B. (2012) Is Classification Necessary after Google? Journal of Documentation, 68, 299-317.
http://dx.doi.org/10.1108/00220411211225557 [6] Tseng, Y., Chang, C., Rundgren. S.C. and Rundgren, C. (2010) Mining Concept Maps from News Stories for Measuring Civic Scientific Literacy in Media. Computers & Education, 55, 165-177.
http://dx.doi.org/10.1016/j.compedu.2010.01.002 [7] Bichindaritz, I. and Akkineni, S. (2006) Concept Mining for Indexing Medical Literature. Engineering Applications of Artificial Intelligence, 19, 411-417. http://dx.doi.org/10.1016/j.engappai.2006.01.009 [8] Ma, L. (2012) Principles of Classification. ALCTS Webinar.
http://downloads.alcts.ala.org/ce/111212_principles_of_classification_slides.pdf [9] Kellogg, D.L. and Walczak, S. (2007) Nurse Scheduling: From Academia to Implementation or Not? Interfaces, 37, 355-369. http://dx.doi.org/10.1287/inte.1070.0291 [10] Susman, G.I, and Evered, R.D. (1978) An Assessment of the Scientific Merits of Action Research. Administrative Science Quarterly, 23, 582-603. http://dx.doi.org/10.2307/2392581 [11] Kohavi, R., Rothleder, N.J. and Simoudis, E. (2002) Emerging Trends in Business Analytics. Communications of the ACM, 45, 45-48. [12] Harzing, A. (2007) Journal Quality List. http://www.harzing.com/jql.htm [13] Likert, R. (1932) A Technique for the Measurement of Attitudes. Archives of Psychology, 22, 1-54. [14] McGraw, K.O. and Wong, S.P. (1996) Forming Inferences about Some Intraclass Correlation Co-efficients. Psychological Methods, 1, 30-46. http://dx.doi.org/10.1037/1082-989X.1.1.30 [15] Shrout, P.E. and Fleiss, J.L. (1979) Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 86, 420-428. http://dx.doi.org/10.1037/0033-2909.86.2.420 [16] Balakrishnan, R., Qui, X.Y. and Srinivasan, P. (2010) On the Predictive Ability of Narrative Disclosures in Annual Reports. European Journal of Operational Research, 202, 789-801.
http://dx.doi.org/10.1016/j.ejor.2009.06.023 [17] Bragge, J., Thavikulwat, P. and Töyli, J. (2010) Profiling 40 Years of Research in Simulation & Gaming. Simulation & Gaming, 41, 869-897. http://dx.doi.org/10.1177/1046878110387539 [18] Porter, A.L., Kongthon, A. and Lu, J.C. (2002) Research Profiling: Improving the Literature Review. Scientometrics, 53, 351-370. http://dx.doi.org/10.1023/A:1014873029258 [19] Raghuram, S., Tuertscher, P. and Garud, R. (2010) Mapping the Field of Virtual Work: A Cocitation Analysis. Information Systems Research, 21, 983-999. http://dx.doi.org/10.1287/isre.1080.0227 [20] Yang, Y., Akers, L., Klose, T. and Yang, C.B. (2008) Text Mining and Visualization Tools—Impressions of Emerging Capabilities. World Patent Information, 30, 280-293.
http://dx.doi.org/10.1016/j.wpi.2008.01.007 [21] Conway, M. (2010) Mining a Corpus of Biographical Texts Using Keywords. Literary and Linguistic Computing, 25, 23-35. http://dx.doi.org/10.1093/llc/fqp035 [22] Glänzel, W. (2012) Bibliometric Methods for Detecting and Analysing Emerging Research Topics. El profesional de la información, 21, 194-201. [23] Seol, H., Lee, S. and Kim, C. (2011) Identifying New Business Areas Using Patent Information: A DEA and Text Mining Approach. Expert Systems with Applications, 38, 2933-2941.
http://dx.doi.org/10.1016/j.eswa.2010.06.083 [24] Fitzgerald, M. (2012) Introducing Regular Expressions. O’Reilly Media, Sebastopol. [25] Kepes, S., Banks, G.C., McDaniel, M. and Whetzel, D.L. (2012) Publication Bias in the Organizational Sciences. Organizational Research Methods, 15, 624-662.
http://dx.doi.org/10.1177/1094428112452760 [26] Anzanello, M.J., Albin, S.L. and Chaovalitwongse, W.A. (2012) Multicriteria Variable Selection for Classification of Production Batches. European Journal of Operational Research, 218, 97-105.
http://dx.doi.org/10.1016/j.ejor.2011.10.015 [27] Bambenek, J. and Klus, A. (2009) grep Pocket Reference. O’Reilly Media, Sebastopol. [28] Elliot, T. (2011) Business Analytics vs Business Intelligence?
http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html [29] Storey, V.C. (2005) Comparing Relationships in Conceptual Modeling: Mapping to Semantic Classifications. IEEE Transactions on Knowledge and Data Engineering, 17, 1478-1489.
http://dx.doi.org/10.1109/TKDE.2005.175
[1] Stringer, M.J., Sales-Pardo, M. and Amaral, L.A.N. (2008) Effectiveness of Journal Ranking Schemes as a Tool for Locating Information. PLoS ONE, 3, 1-8. [2] García-Crespo, á., Gómez-Berbís, J.M., Colomo-Palacios, R. and García-Sánchez, F. (2011) Digital Libraries and Web 3.0. The Callimachus DL Approach. Computers in Human Behavior, 27, 1424-1430.
http://dx.doi.org/10.1016/j.chb.2010.07.046 [3] Lee, J.Y., Kim, H. and Kim, P.J. (2010) Domain Analysis with Text Mining: Analysis of Digital Library Research Trends Using Profiling Methods. Journal of Information Science, 36, 144-161.
http://dx.doi.org/10.1177/0165551509353251 [4] Ma, J., Xu, W., Sun, Y., Turban, E., Wang, S. and Liu, O. (2012) An Ontology-Based Text Analytics Method to Cluster Proposals for Research Project Selection. Transactions on Systems, Man, and Cybernetics—Part A, 42, 784-790.http://dx.doi.org/10.1109/TSMCA.2011.2172205 [5] Hjørland, B. (2012) Is Classification Necessary after Google? Journal of Documentation, 68, 299-317.
http://dx.doi.org/10.1108/00220411211225557 [6] Tseng, Y., Chang, C., Rundgren. S.C. and Rundgren, C. (2010) Mining Concept Maps from News Stories for Measuring Civic Scientific Literacy in Media. Computers & Education, 55, 165-177.
http://dx.doi.org/10.1016/j.compedu.2010.01.002 [7] Bichindaritz, I. and Akkineni, S. (2006) Concept Mining for Indexing Medical Literature. Engineering Applications of Artificial Intelligence, 19, 411-417. http://dx.doi.org/10.1016/j.engappai.2006.01.009 [8] Ma, L. (2012) Principles of Classification. ALCTS Webinar.
http://downloads.alcts.ala.org/ce/111212_principles_of_classification_slides.pdf [9] Kellogg, D.L. and Walczak, S. (2007) Nurse Scheduling: From Academia to Implementation or Not? Interfaces, 37, 355-369. http://dx.doi.org/10.1287/inte.1070.0291 [10] Susman, G.I, and Evered, R.D. (1978) An Assessment of the Scientific Merits of Action Research. Administrative Science Quarterly, 23, 582-603. http://dx.doi.org/10.2307/2392581 [11] Kohavi, R., Rothleder, N.J. and Simoudis, E. (2002) Emerging Trends in Business Analytics. Communications of the ACM, 45, 45-48. [12] Harzing, A. (2007) Journal Quality List. http://www.harzing.com/jql.htm [13] Likert, R. (1932) A Technique for the Measurement of Attitudes. Archives of Psychology, 22, 1-54. [14] McGraw, K.O. and Wong, S.P. (1996) Forming Inferences about Some Intraclass Correlation Co-efficients. Psychological Methods, 1, 30-46. http://dx.doi.org/10.1037/1082-989X.1.1.30 [15] Shrout, P.E. and Fleiss, J.L. (1979) Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 86, 420-428. http://dx.doi.org/10.1037/0033-2909.86.2.420 [16] Balakrishnan, R., Qui, X.Y. and Srinivasan, P. (2010) On the Predictive Ability of Narrative Disclosures in Annual Reports. European Journal of Operational Research, 202, 789-801.
http://dx.doi.org/10.1016/j.ejor.2009.06.023 [17] Bragge, J., Thavikulwat, P. and Töyli, J. (2010) Profiling 40 Years of Research in Simulation & Gaming. Simulation & Gaming, 41, 869-897. http://dx.doi.org/10.1177/1046878110387539 [18] Porter, A.L., Kongthon, A. and Lu, J.C. (2002) Research Profiling: Improving the Literature Review. Scientometrics, 53, 351-370. http://dx.doi.org/10.1023/A:1014873029258 [19] Raghuram, S., Tuertscher, P. and Garud, R. (2010) Mapping the Field of Virtual Work: A Cocitation Analysis. Information Systems Research, 21, 983-999. http://dx.doi.org/10.1287/isre.1080.0227 [20] Yang, Y., Akers, L., Klose, T. and Yang, C.B. (2008) Text Mining and Visualization Tools—Impressions of Emerging Capabilities. World Patent Information, 30, 280-293.
http://dx.doi.org/10.1016/j.wpi.2008.01.007 [21] Conway, M. (2010) Mining a Corpus of Biographical Texts Using Keywords. Literary and Linguistic Computing, 25, 23-35. http://dx.doi.org/10.1093/llc/fqp035 [22] Glänzel, W. (2012) Bibliometric Methods for Detecting and Analysing Emerging Research Topics. El profesional de la información, 21, 194-201. [23] Seol, H., Lee, S. and Kim, C. (2011) Identifying New Business Areas Using Patent Information: A DEA and Text Mining Approach. Expert Systems with Applications, 38, 2933-2941.
http://dx.doi.org/10.1016/j.eswa.2010.06.083 [24] Fitzgerald, M. (2012) Introducing Regular Expressions. O’Reilly Media, Sebastopol. [25] Kepes, S., Banks, G.C., McDaniel, M. and Whetzel, D.L. (2012) Publication Bias in the Organizational Sciences. Organizational Research Methods, 15, 624-662.
http://dx.doi.org/10.1177/1094428112452760 [26] Anzanello, M.J., Albin, S.L. and Chaovalitwongse, W.A. (2012) Multicriteria Variable Selection for Classification of Production Batches. European Journal of Operational Research, 218, 97-105.
http://dx.doi.org/10.1016/j.ejor.2011.10.015 [27] Bambenek, J. and Klus, A. (2009) grep Pocket Reference. O’Reilly Media, Sebastopol. [28] Elliot, T. (2011) Business Analytics vs Business Intelligence?
http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html [29] Storey, V.C. (2005) Comparing Relationships in Conceptual Modeling: Mapping to Semantic Classifications. IEEE Transactions on Knowledge and Data Engineering, 17, 1478-1489.
http://dx.doi.org/10.1109/TKDE.2005.175