Location
260-005, Owen G. Glenn Building
Start Date
12-15-2014
Description
Online consumer reviews (OCR) have helped consumers to know about the strengths and weaknesses of different products and find the ones that best suit their needs. This research investigates the predictors of readership and helpfulness of OCR using a sentiment mining approach. Our findings show that reviews with higher levels of positive sentiment in the title receive more readerships. Sentimental reviews with neutral polarity in the text are also perceived to be more helpful. The length and longevity of a review positively influence both its readership and helpfulness. Our findings suggest that the current methods used for sorting OCR may bias both their readership and helpfulness. This study can be used by online vendors to develop scalable automated systems for sorting and classification of OCR which will benefit both vendors and consumers.
Recommended Citation
Salehan, Mohammad and Kim, Dan, "Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach" (2014). ICIS 2014 Proceedings. 8.
https://aisel.aisnet.org/icis2014/proceedings/DecisionAnalytics/8
Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach
260-005, Owen G. Glenn Building
Online consumer reviews (OCR) have helped consumers to know about the strengths and weaknesses of different products and find the ones that best suit their needs. This research investigates the predictors of readership and helpfulness of OCR using a sentiment mining approach. Our findings show that reviews with higher levels of positive sentiment in the title receive more readerships. Sentimental reviews with neutral polarity in the text are also perceived to be more helpful. The length and longevity of a review positively influence both its readership and helpfulness. Our findings suggest that the current methods used for sorting OCR may bias both their readership and helpfulness. This study can be used by online vendors to develop scalable automated systems for sorting and classification of OCR which will benefit both vendors and consumers.