Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing AttributesAuthor(s):
Publication Date: May 2019
Revision Date: June 2021
The authors address two signiﬁcant challenges in using online text reviews to obtain ﬁnegrained attribute level sentiment ratings. First, in contrast to methods that rely on word frequency, they develop a deep learning convolutional-LSTM hybrid model to account for language structure. The convolutional layer accounts for spatial structure (adjacent word groups or phrases) and LSTM accounts for sequential structure of language (sentiment distributed and modiﬁed across non-adjacent phrases). Second, they address the problem of missing attributes in text in constructing 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 attribute sentiment scoring accuracy with their model. They ﬁnd three reviewer segments with diﬀerent motivations: status seeking, altruism/want voice, and need to vent/praise. Reviewers write to inform and vent/praise, but not based on attribute importance. The heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially signiﬁcant corrections in restaurant attribute ratings. More broadly, our results suggest that social science research should pay more attention to reduce measurement error in variables constructed from text.
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 2176CFDP 2176R