Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products
Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers.
Chaffar, S. & Inkpen, D., Using a Heterogeneous Dataset for Emotion Analysis in Text, Advances in Artificial Intelligence, Canadian AI 2011, Lecture Notes in Computer Science, 6657, pp. 62-67, Springer, Berlin, Heidelberg, 2011.
Nasukawa, T. & Yi, J., Sentiment analysis: Capturing favorability using natural language processing, Proceedings of the 2nd International Conference on Knowledge Capture, pp. 70-77, October, 2003.
Neviarouskaya, A., Prendinger, H. & Ishizuka, M., Textual affect sensing for Sociable and Expressive Online Communication, International Conference on Affective Computing and Intelligent Interaction, pp. 218-229, 2007.
Hao, M.C., Rohrdantz, C., Janetzko, H., Keim, D.A., Dayal, U., Haug, L.E., Hsu, M. & Stoffel, F., Visual Sentiment Analysis of Customer Feedback Streams Using Geo-temporal Term Associations, Information Visualization, 12(3-4), pp. 273-290, July. 2013.
Eciolaza, L., Pereira-Fariña, M. & Trivino, G., Automatic Linguistic Reporting in Driving Simulation Environments, Applied Soft Computing, 13(9), pp. 3956-3967, September. 2013.
Conde-Clemente, P., Alonso, J.M., Nunes, É.O., Sanchez, A. & Trivino, G., New Types of Computational Perceptions: Linguistic Descriptions in Deforestation Analysis, Expert Systems with Applications, 85, pp. 46-60, 2017.
Kostyra, D.S., Reiner, J., Natter, M. & Klapper, D., Decomposing the Effects of Online Customer Reviews on Brand, Price, and Product Attributes, International Journal of Research in Marketing, 33(1), pp.11-26, 2016.
Bucur, C., Using Opinion Mining Techniques in Tourism, Procedia Economics and Finance, 23, pp. 1666-1673, January. 2015.
Parsehub, parsehub.com, Accessed from https://www.parsehub.com/ (27 May 2018).
Saif M., saifmohammad.com, Accessed from http://saifmohammad.com/ WebPages/NRC-Emotion-Lexicon.htm (25 June 2018).
Liu B., cs.uic.edu, Accessed from https://www.cs.uic.edu/~liub/FBS/ sentiment-analysis.html (2 July 2018).
R-project, r-project.org, Accessed from https://www.r-project.org/ (25 June 2018).
Plutchik, R., The Nature of Emotions: Human Emotions Have Deep Evolutionary Roots, a Fact that may Explain Their Complexity and Provide Tools for Clinical Practice, American Scientist, 89(4), pp. 344-350, 2001.
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