Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Attribute Self-Selection

55 Pages Posted: 29 May 2019 Last revised: 12 Jun 2019

See all articles by Ishita Chakraborty

Ishita Chakraborty

Yale School of Management

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: May 27, 2019

Abstract

The authors address two novel and significant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selection—reviewers choose the subset of attributes to write about—in metrics of attribute level restaurant performance. Using Yelp.com reviews for empirical illustration, they find that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the “hard” sentiment classification problems. Further, accounting for attribute self-selection significantly impacts sentiment scores, especially on attributes that are frequently missing.

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 Attribute Self-Selection (May 27, 2019). 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

Ishita Chakraborty (Contact Author)

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

Minkyung Kim

UNC Chapel Hill Kenan-Flagler Business School ( email )

300 Kenan Drive
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

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