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Social opinion mining for supporting buyers’ complex decision making: exploratory user study and algorithm comparison

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Abstract

This article reports our study of the role of social content (i.e., user-generated content in social networking environment) in online consumers’ decision process when they search for an inexperienced product to buy. Through close observation of users’ objective behavior and interview of their reflective thoughts during an initial exploratory user study, we have first derived a set of system implications and integrated these implications into a three-stage system architecture. Furthermore, driven by the specific implication regarding the impact of user reviews in influencing users’ decision stages, we have presented a linear-chain conditional random-field-based social-opinion-mining algorithm, and have identified its higher effectiveness against related algorithms in an experiment. Finally, we present our system’s user interfaces and emphasize on how to display the opinion-mining results in the form of both quantitative presentation and qualitative visualization.

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Notes

  1. Please note that the “feature” here refers to the product’s feature. It is different from the definition of feature in feature functions (for which we will discuss later when constructing CRFs model).

  2. http://l2r.cs.uiuc.edu/~cogcomp/software.php.

  3. http://www.cs.cornell.edu/People/pabo/movie-review-data/review_polarity.tar.gz.

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  1. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

    Li Chen & Luole Qi

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  1. Li Chen
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  2. Luole Qi
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Correspondence to Li Chen.

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Chen, L., Qi, L. Social opinion mining for supporting buyers’ complex decision making: exploratory user study and algorithm comparison. Soc. Netw. Anal. Min. 1, 301–320 (2011). https://doi.org/10.1007/s13278-011-0023-y

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