Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-SelectionAuthor(s):
Publication Date: May 2019
Revision Date: September 2020
The authors address two signiﬁcant challenges in using online text reviews to obtain ﬁne-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modiﬁed across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model ﬁnds three reviewer segments with diﬀerent motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially signiﬁcant corrections in restaurant attribute ratings.
Text mining, Natural language processing (NLP), Convolutional neural networks (CNN), Long-short term memory (LSTM) Networks, Deep learning, Lexicons, Endogeneity, Self-selection, Online reviews, Online ratings, Customer satisfaction
JEL Classification Codes: M1, M3, C8, C5See CFDP Version(s): CFDP 2176