- Volume 17 Issue 11
DOI QR Code
Clustering Corporate Brands based on Opinion Mining: A Case Study of the Automobile Industry
오피니언 마이닝을 통한 브랜드 클러스터링: 자동차 산업 사례연구
- Hwang, Hyun-Seok (Dept. of Business Administration, Hallym University)
- 황현석 (한림대학교 경영학과)
- Received : 2016.07.29
- Accepted : 2016.11.10
- Published : 2016.11.30
Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.
Automobile Industry;Brand clustering;Multidimensional Scaling;Opinion Mining;Social Media
Supported by : 한림대학교, 정보통신기술진흥센터
- B. Pang, L. Lee, S. Vaithyanathan, "Thumbs up? Sentiment Classification using Machine Learning Techniques", Proc. of the ACL-02 Conference on Empirical Methods in Natural Language Processing, pp. 79-86, 2002. DOI: http://dx.doi.org/10.3115/1118693.1118704
- W. Wang, "Sentiment analysis of online product reviews with semi-supervisied topic sentiment mixture model", Proc. of 7th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 5, pp. 2385-2389, 2010.
- H.-X, Shi, X.-J. Li, "A sentiment analysis model for hotel reviews based on supervised learning", Proc. of International Conference on Machine Learning and Cybernetics, vol. 3, pp. 950-954, 2011.
- P. D. Turney, "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews", Proc. of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417-424, 2002.
- M. Hu, B. Liu, "Mining and Summarizing Customer Reviews", Proc. of International conference on Knowledge discovery and data mining, pp. 168-177, 2004. DOI: http://dx.doi.org/10.1145/1014052.1014073 https://doi.org/10.1145/1014052.1014073
- B. Liu, Sentiment Analysis and Opinion Mining, Morgan and Claypool Publishers, pp. 168, 2012.
- A. Bifet, G. Holmes, B. Pfahringer, "MOA-Tweet Reader: real-time analysis in twitter Streaming data", Lecture Notes in Computer Science, vol. 6926, pp. 46-60, 2011. DOI: http://dx.doi.org/10.1007/978-3-642-24477-3_7 https://doi.org/10.1007/978-3-642-24477-3_7
- L. de Vries, S. Gensler, P. S. H. Leeflang, "Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing", Journal of interactive marketing, vol. 26, no. 3, pp. 83-91, 2012. DOI: http://dx.doi.org/10.1016/j.intmar.2012.01.003 https://doi.org/10.1016/j.intmar.2012.01.003
- A. Tedeschi, F. Benedetto, "A cloud-based big data sentiment analysis application for enterprises' brand monitoring in social media streams", IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow, pp. 186-191, 2015. DOI: http://dx.doi.org/10.1109/rtsi.2015.7325096 https://doi.org/10.1109/rtsi.2015.7325096
- E. Adeborna, A. Funk, "An approach to sentiment analysis the case of airline quality rating", Proc. of the Pacific Asia Conference on Information Systems, pp. 363-368, 2014.
- F. H. Khan, S. Bashir, U. Qamar, "TOM: Twitter opinion mining framework using hybrid classification scheme", Decision support systems, vol. 57, pp. 245-257, 2014. DOI: http://dx.doi.org/10.1016/j.dss.2013.09.004 https://doi.org/10.1016/j.dss.2013.09.004
- M. Ghiassi, J. Skinner, D. Zimbra, "Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network", Expert systems with applications, vol. 40, no. 16, pp. 6266-6282, 2013. DOI: http://dx.doi.org/10.1016/j.eswa.2013.05.057 https://doi.org/10.1016/j.eswa.2013.05.057
- B. Lu, B. Tsou, "Combining a large sentiment lexicon and machine for subjectivity classification", Proc. of International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3311-3316, 2010. DOI: http://dx.doi.org/10.1109/icmlc.2010.5580672
- A. Z. H. KHAN, M. ATIQUE, V. M. THAKARE, "Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis", International Journal of Electronics,Communication and Soft Computing Science and Engineering, Special Issue of IJECSCSE, pp. 89-91, 2015.
- L. Zhang, R. Ghosh, M. Dekhil, M. Hsu, B. Liu, "Combining lexicon-based and learning-based methods for twitter sentiment analysis", http://www.hpl.hp.com/techreports/2011/HPL-2011-89.pdf, 2011.
- P. P. B. Filho, L. V. Avanco, M. d. G. V. Nunes, T. A. S. Pardo, "NILC_USP: An Improved hybrid system for sentiment analysis in twitter message", Proc. of International Workshop on Semantic Evaluation, pp. 428-432, 2014.
- M. M. Mostafa, "More than words: Social networks' text mining for consumer brand sentiments", Expert systems with applications, vol. 40, no. 10, pp. 4241-4251, 2013. DOI: http://dx.doi.org/10.1016/j.eswa.2013.01.019 https://doi.org/10.1016/j.eswa.2013.01.019
- D. Arora, F. L. Kin, Stephen W. Neville, "Consumers' Sentiment Analysis of Popular Phone Brands and Operating System Preference Using Twitter Data: A Feasibility Study", Proc. of International Conference on Advanced Information Networking and Applications, pp. 680-686, 2015.
- S. Lihui, X. Tianshu, "On the Impact of Brand Cluster Effects of Forming of Place Brand: Take Wenzhou Cluster for Example", Proc. of International Conference on E-Business and E-Government, pp. 5351-5354, 2010. DOI: http://dx.doi.org/10.1109/icee.2010.1339 https://doi.org/10.1109/icee.2010.1339
- J. A. Mazanec, "Simultaneous positioning and segmentation analysis with topologically ordered feature maps: a tour operator example", Journal of retailing and consumer services vol. 6, no. 4, pp. 219-235, 1999. DOI: http://dx.doi.org/10.1016/S0969-6989(98)00037-X https://doi.org/10.1016/S0969-6989(98)00037-X
- S.-P. Jun, D.-H. Park, "Intelligent Brand Positioning Visualization System Based on Web Search Traffic Information : Focusing on Tablet PC", Journal of intelligence and information systems, vol. 19, no. 3, pp. 93-111, 2013. DOI: http://dx.doi.org/10.13088/jiis.2013.19.3.093 https://doi.org/10.13088/jiis.2013.19.3.093
- G.-R. Kim, "The positionings of coffee brands and market segmentation based on perceived benefits", Master's thesis, Young Nam University, 2010.
- H. S. Cho, "A Study on the positioning of Overseas Resorts based on Resort selection attributes by Multi-dimensional Scaling Method", Master's thesis, Kyunghee University, 2015.
- Y.-Y. Lee, E. Yoo, J.-Y. Ko, "The Brand Positioning for Wine Importers in Korea : Focused on B2B", Journal of tourism and leisure research, vol. 24, no. 1, pp. 433-452. 2012.
- C. M. Lim, R. Runyan, Y.-K. Kim, "Segmenting luxe-bargain shoppers using a fuzzy clustering method", International Journal of Retail & Distribution Management, vol. 41, no. 11/12, pp. 848-868, 2013. DOI: http://dx.doi.org/10.1108/IJRDM-01-2013-0012 https://doi.org/10.1108/IJRDM-01-2013-0012
- J. Yoon, J. Yim, "The Market Positioning Analysis of Social Network Services", Korean journal of broadcasting, vol. 26, no. 3, pp. 416-457, 2012.
- S. Kim, "A Classification of Luxury Fashion Brands' E-commerce Sites", Fashion business vol. 17, no. 6, pp. 125-140, 2013. https://doi.org/10.12940/jfb.2013.17.6.125
- An Analysis of IT Proposal Evaluation Results using Big Data-based Opinion Mining vol.41, pp.1, 2018, https://doi.org/10.11627/jkise.2018.41.1.001