Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2022.2106282
Learning consumer preferences from online textual reviews and ratings based on the aggregation-disaggregation paradigm with attitudinal Choquet integral
Qian Yang
Bing Zhu
Huchang Liao
Xingli Wu
Sažetak
Online reviews contain a wealth of information about customers’ concerns
and sentiments. Sentiment analysis can mine consumer preferences
and satisfaction over products/services. Most existing studies on
sentiment analysis only considered how to extract attribute types or
attribute values of products/services from textual reviews, but ignored
the role of attribute-level ratings in reflecting consumer preferences
and satisfaction. Based on sentiment analysis and preference disaggregation,
this paper unifies the quantitative and qualitative information
extracted from attribute-level ratings and textual reviews, respectively,
to obtain attribute types and attribute values of products/services. To
acquire individual consumer preferences concerning product/service
attributes, this paper proposes a method within an aggregation-disaggregation
paradigm based on the attitudinal Choquet integral to
transform overall online ratings into the form of pairwise comparisons.
Compared with the additive value function used in most studies, more
consumer preferences in terms of the importance of attributes, the
interactions between pairwise attributes, and the tolerance of consumers
to make compensation between attribute values in the aggregation
process can be deduced by our proposed method. Several real
cases on TripAdvisor.com are given to show the applicability of the
proposed method.
Ključne riječi
Online reviews; sentiment analysis; attitudinal Choquet integral; multiple attribute decision making; robust original regression
Hrčak ID:
306630
URI
Datum izdavanja:
30.4.2023.
Posjeta: 155 *