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Customer sentiment appraisal from user-generated product reviews: a domain independent heuristic algorithm

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Abstract

Social media give new opportunities in customer survey and market survey for design inspiration with comments posted online by users spontaneously, in an oral-near language, and almost free of biases. Opinion mining techniques are being developed, especially customer sentiment analysis. These techniques are most of the time based on a text parsing and costly learning techniques based on target or domain-dependent corpora for getting a fine understanding of users’ preferences. On the contrary, in this paper, we propose an overall sentiment rating algorithm, accurate enough to deliver an overall rating on a product review, without a tedious customization to a product domain or customer polarities. The developed algorithm starts by a text parsing, uses a Dictionary of Affect Language to rate the word tree leaves and uses a series of basic heuristics to calculate backward an overall sentiment rating for the review. We validate it on the example of a commercial home theatre system, comparing our automated sentiment predictions with the one of a group of fifteen test subjects, resulting in a satisfactory correlation.

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Authors and Affiliations

  1. Ecole Centrale Paris, Laboratoire Genie Industriel, Grande voie des Vignes, 92290, Chatenay-Malabry, France

    Dilip Raghupathi, Bernard Yannou & Romain Farel

  2. Ecole Centrale de Nantes, IRCCYN, 1 rue de la Noe, BP 92101, 44 321, Nantes Cedex 3, France

    Emilie Poirson

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  1. Dilip Raghupathi
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  2. Bernard Yannou
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  3. Romain Farel
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  4. Emilie Poirson
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Correspondence to Bernard Yannou.

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Raghupathi, D., Yannou, B., Farel, R. et al. Customer sentiment appraisal from user-generated product reviews: a domain independent heuristic algorithm. Int J Interact Des Manuf 9, 201–211 (2015). https://doi.org/10.1007/s12008-015-0273-4

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  • DOI: https://doi.org/10.1007/s12008-015-0273-4

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