Abstract
The Internet is a reality in people’s lives, enabling the growth of many online services companies. However, to maintain their activities and stay in the market, it’s important for these companies to worry about the quality of the provided services. In this context, it becomes important to be able to assess the client satisfaction regarding those services. The objective of this work is to propose a tool for aiding the evaluation of customer satisfaction in a Brazilian Online Job Search Company through the use of Sentiment Analysis. Sentiment Analysis, or Opinion Mining, refers to the techniques used to extract and evaluate sentiment expressed in textual data. We analyzed a database of an online job search company containing client comments collected from a service cancellation form. This database, among other parameters, has a score assigned by the client and a comment about the services. We performed the classification of the sentiment expressed in the user comments with the aid of a software written in Python, and then calculated the correlation of the sentiment score with the score assigned by the clients. The results lead to the conclusion that the use of Sentiment Analysis serves as a support tool to enrich the customer satisfaction assessment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pinho, J.A.G.: Information society, capitalism and civil society: reflections on politics, the internet and democracy in the brazilian reality. Revista de Administração de Empresas 51(1), 98–106 (2011)
Parassuraman, A., Zeithaml, V.A., Berry, L.L.: A conceptual model of service quality and its implications for future research. J. Mark. 49, 41–50 (1985)
Zeithaml, V.A., Parasuraman, A., Malhotra, A.: Service quality delivery through web sites: a critical review of extant knowledge. J. Acad. Mark. Sci. 30(4), 362–375 (2002)
Chen, H., Zimbra, D.: AI and opinion mining. Intell. Syst. 3(25), 74–80 (2010)
Tontini, G., Sant’ana, A.: Interaction of basic and excitement service attributes in customer satisfaction. Production 18(1), 112–125 (2008)
Berry, L.L.: Services marketing is different. Business 30, 24–28 (1980)
Booms, B.H., Bitner, M.J.: Marketing strategies and organization structures for services firms, marketing of services. In: Donnelly, J., George, W. (eds.) marketing of services, pp. 47–51. American Marketing Association, Chicago (1981)
Upah, G.D.: Mass marketing in service retailing: a review and synthesis of major methods. J. Retail. 56, 59–76 (1980)
Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing. Taylor and francis, Boca (2010)
Valarmathi, B., Palanisamy, V.: Opinion mining classification using key word summarization based on singular value decomposition. Int. J. Comput. Sci. Eng. 3(1), 212–215 (2011)
Wang, W., Zhou, Y.: E-Business websites evaluation based on opinion mining. In: International Conference on Electronic Commerce and Business Intelligence, pp 87–90 (2009)
Liu, B., Hu, M.: Mining opinion features in customer reviews. In: Proceedings of Nineteenth National Conference on Artificial Intelligence (AAAI-2004), pp. 755–760 (2004)
Liu, B.: Sentiment Analysis and Opinion Mining: Synthesis Lectures on Human Language Technologies, vol. 16. Morgan & Claypool Publishers, San Rafael (2012)
Miller, G.A.: WordNet: A lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Brooke, J., Tofiloski, M., Taboada, M.: Cross-linguistic sentiment analysis: From english to spanish. In: International Conference RANLP, pp. 50–54 (2009)
Wan, X.: Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 553–561. Association for Computational Linguistics (2008)
Wan, X.: Co-training for cross-lingual sentiment classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: vol. 1, pp. 235–243. Association for Computational Linguistics (2009)
Denecke, K.: Using sentiwordnet for multilingual sentiment analysis. In: IEEE 24th International Conference on pp. 507–512 Data Engineering Workshop, 2008, ICDEW 2008. IEEE (2008)
Google Translate. http://translate.google.com
Google Translate API. https://developers.google.com/translate
JSON. http://www.json.org
Repustate: Sentiment analysis and social media analytics. https://www.repustate.com
Python Foundation. http://www.python.org
Repustate API documentation. https://www.repustate.com/docs/#api-2
MySQL. http://www.mysql.com
Cohen, J.: Statistical power analysis for behavioral sciences. Hillsdale, NJ, Erlbaum (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Miranda, M.D., Sassi, R.J. (2014). Using Sentiment Analysis to Assess Customer Satisfaction in an Online Job Search Company. In: Abramowicz, W., Kokkinaki, A. (eds) Business Information Systems Workshops. BIS 2014. Lecture Notes in Business Information Processing, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-319-11460-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-11460-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11459-0
Online ISBN: 978-3-319-11460-6
eBook Packages: Computer ScienceComputer Science (R0)