Abstract
Measuring customers’ opinions based on online customer reviews pose an integral part of Social CRM. However, polarity analysis, i.e., positive vs. negative opinion, fails to map the emotional mindset of customers. To complement existing Social CRM tools with a comprehensible, yet efficient way of measuring emotions towards brands, a model is presented to differentiate eight basic human emotions. Emotion terms get extracted and categorized review-wise by an eight dimensional emotion lexicon into eight dimensional feature vectors. These vectors train the random forest classifier to distinguish positive helpful from negative helpful reviews. The classifiers inherent ability to display single feature importance enables marketers to infer the importance of each basic emotion. The ability to measure the interrelationship of emotions towards brands equips marketers with a powerful tool to better understand consumers and to adapt CRM campaigns accordingly. Along with the technicalities of the model a way of interpreting results is presented.
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Notes
- 1.
We follow the assumption that, as is typical in many e-commerce sites, customers can vote a review as helpful or not helpful.
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161–168. ACM (2006)
Kapferer, J.-N.: The New Strategic Brand Management: Advanced Insights and Strategic Thinking. Kogan Page Publishers (2012)
Martin, L., Sintsova, V., Pearl, P.: Are influential writers more objective? An analysis of emotionality in review comments. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 799–804. International World Wide Web Conferences Steering Committee (2014)
Maynard, D., Bontcheva, K., Rout, D.: Challenges in developing opinion mining tools for social media. In: Proceedings of the@ NLP can u tag# usergeneratedcontent, pp. 15–22 (2012)
Scott, M., Alan, Z., Daniel, L.: The New Science of Customer Emotions. Harvard Business School Publishing Corporation (2015)
McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 165–172. ACM (2013)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
Plutchik, R.: A General Psychoevolutionary Theory of Emotion. In: Theories of Emotion, vol. 1 (1980)
Olaf, R., Rainer, A.: Social Customer Relationship Management: State of the Art and Learnings from Current Projects (2012)
Scherer, K.R.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695–729 (2005)
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Felbermayr, A. (2017). Emotions in Online Reviews to Better Understand Customers’ Brand Perception. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-52464-1_21
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DOI: https://doi.org/10.1007/978-3-319-52464-1_21
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