Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach

By Qing Cao, Wenjing Duan, Qiwei Gan. Decision Support Systems, 2011

687 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1d26ea648df63a7ded8457539e8ff4f1aa68b740

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Citations in the corpus

Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features (2013, background): “, [9]), most of these studies have typically considered the most popular reviews as helpful ones [75], but scant attention has been paid to investigating the types of reviews that are actually helpful for a consumer.”
Anxious or Angry? Effects of Discrete Emotions on the Perceived Helpfulness of Online Reviews (2014, background): “Consumers often require only a small set of helpful reviews, and many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010).”
Sentiment analysis algorithms and applications: A survey (2014, methodology): “Table 1 contains the articles reference [4–7] and [12–61].”
Keep Your Cool or Let it Out: Nonlinear Effects of Expressed Arousal on Perceptions of Consumer Reviews (2017, background): “However, an emerging trend has begun to explore review content more directly (Cao, Duan, and Gan 2011; Moore 2015; Yin, Bond, and Zhang 2014).”
Mining customer requirements from online reviews: A product improvement perspective (2016, background): “Previous research has found that many online marketplaces provide mechanisms for consumers to identify helpful reviews [19, 37, 52, 53].”
Operational Transparency on Crowdfunding Platforms: Effect on Donations for Emergency Response (2019, background): “…and Frank 2004, Li 2010, Loughran and Mcdonald 2011, Schumaker and Chen 2009, Tetlock et al. 2008), to forecast customer preferences and choice (Cao et al. 2011, Decker and Trusov 2010, Li and Wu 2010, Netzer et al. 2012) and to identify components and potential product defects across supply…”
Stimulating Online Reviews by Combining Financial Incentives and Social Norms (2017, positive): “Indeed, the vast majority of online reviews are brief and lack useful information (Cao et al. 2011; Liu et al. 2007; Mudambi and Schuff 2010).”
Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services (2018, background): “4 proportion of reviews, particularly more recent ones, have few or no helpfulness votes [11].”
A survey on sentiment analysis methods, applications, and challenges (2022, methodology): “In work of Cao et al. (2011) in used LSA to find semantic qualities from reviews to investigate the effect of various features.”
Predicting the “helpfulness” of online consumer reviews (2017, methodology): “The “helpfulness” feature of online user reviewsmakes it easier for consumers to copewith information overload and helps in their decision-making process (Cao et al., 2011; Krishnamoorthy, 2015).”
A study of factors that contribute to online review helpfulness (2015, methodology): “…reviews, ‘‘review helpfulness’’ is particularly important, as it represents the subjective valuation of the review judged by others, and is also the aggregate perceived utility of the information contained in the review (Cao et al., 2011; Baek, Ahn, & Choi, 2012; Li, Huang, Tan, & Wei, 2013).”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “While the number and types of information resources continue to grow exponentially, human beings start to face the difficulty of transforming thewealth of information to knowledge formore effective use [3].”