Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

Posted: 29 May 2019 Last revised: 27 Sep 2021

See all articles by Ishita Chakraborty

Ishita Chakraborty

UW Madison

Minkyung Kim

UNC Chapel Hill Kenan-Flagler Business School

K. Sudhir

Yale School of Management; Yale University-Department of Economics; Yale University - Cowles Foundation

Date Written: September 15, 2020

Abstract

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.

Keywords: 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: M1, M3, C8, C5

Suggested Citation

Chakraborty, Ishita and Kim, Minkyung and Sudhir, K., Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes (September 15, 2020). Cowles Foundation Discussion Paper No. 2176, March 2019, Available at SSRN: https://ssrn.com/abstract=3395012 or http://dx.doi.org/10.2139/ssrn.3395012

Minkyung Kim

UNC Chapel Hill Kenan-Flagler Business School ( email )

Chapel Hill, NC 27599
United States

K. Sudhir

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States
203-432-3289 (Phone)
203-432-3003 (Fax)

Yale University-Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
2,015
PlumX Metrics