Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics

By Mohammad Salehan, Dan J Kim. Decision Support Systems, 2014

585 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/287a9493f9bb5261c57f4528ece12069dac563fe

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Citations in the corpus

Mining customer requirements from online reviews: A product improvement perspective (2016, background): “The literature has used a variety of measures to measure the performance of online reviews: purchase intentions [49], readership [50], and sales revenue [10, 44, 51].”
Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “…2016; Ma et al., 2014 Demand sensing Berengueres and Efimov, 2014; Chong et al., 2016; Fang and Zhan, 2015; He et al., 2015; Li, Ch’ng, et al., 2016; Salehan and Kim, 2016; Wang et al., 2014 Demand shaping Chong et al., 2016; He et al., 2015; Marine-Roig and Anton Clavé, 2015; Salehan and Kim,…”
The value of online surveys: a look back and a look ahead (2018, background): “, 2017; Zhang, Ding and Bian, 2017), sentiment analysis (Salehan and Kim, 2016) and the dark side of reviews (Liu and Karahanna, 2017; Zhang et al.”
Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives (2016, background): “…management (CRM), increase profit margin, find potential market, and carry out various predictions from the service and manufacturing sectors (Lardinois, 2014; Lohman, 2013; Salehan & Kim, 2016; Talia, 2013; Vera-Baquero, Colomo-Palacios, & Molloy, 2013; Zhong, Xu, Chen, & Huang, 2015).”
A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews (2021, background): “Therefore, whether the content of a review is predominantly positive or negative, the perceived helpfulness of the review diminishes in cases of extreme sentimental tone (Salehan & Kim, 2016).”
Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews (2019, background): “In turn, review experts often seek more helpfulness votes from future potential customers and more readership in recognition of their contribution and impact in the review community (Salehan and Kim, 2016).”
Online Review Helpfulness: Role of Qualitative Factors (2016, background): “Thus, as long as sentimental tone is neither extremely positive or extremely negative, consumers should find review as helpful, irrespective of it being a positive or a negative review (Hu et. al, 2012; Salehan and Kim, 2016; Schlosser, 2011).”