Low-Quality Product Review Detection in Opinion Summarization

By Jingjing Liu, Yunbo Cao, Chin-Yew Lin, Yalou Huang, M. Zhou. Conference on Empirical Methods in Natural Language Processing, 2007

423 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4fba12c75d28e46e3ea93102499fb0c27d360e17

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Citations in the corpus

Spotting fake reviewer groups in consumer reviews (2012, background): “Lastly, [18, 25, 37] studied the usefulness or quality of reviews.”
Learning to Identify Review Spam (2011, background): “There are a lot of research papers on review quality prediction [Kim et al., 2006; Liu et al., 2007].”
What Yelp Fake Review Filter Might Be Doing? (2013, background): “Studies on review quality (Liu et al., 2007), distortion (Wu et al., 2010), and helpfulness (Kim et al., 2006) have also been conducted.”
Synthetic review spamming and defense (2013, methodology): “We therefore also tested a quality-examination method proposed in [15], which extracts statistical features to measure the quality of product reviews.”
Quality evaluation of product reviews using an information quality framework (2011, background): “[21] conducted a detailed survey of reviews posted on Amazon and found that users' votes were influenced by three types of bias: imbalanced vote bias, winner circle bias, and early bird bias.”
Analysis of the Perceived Value of Online Tourism Reviews: Influence of Readability and Reviewer Characteristics (2016, background): “Review length will influence perceived helpfulness significantly by exploring the effect of length according to machine learning approaches such as support vector machine, review length is found to influence perceived helpfulness significantly (Jeon et al., 2006; Kim, Pantel, Chklovski,& Pennacchiot”
Opinion Mining and Sentiment Analysis (2008, background): “[193] call this tendency for often-evaluated reviews to quickly accumulate even more utility evaluations as “winner circle” bias; in other literature on power-law effects, related phenomena are also referred to as “rich-get-richer”.”
How Community Feedback Shapes User Behavior (2014, background): “…is concerned with predicting the helpfulness ratings of product reviews starting from textual and social factors (Ghose and Ipeirotis 2007; Liu et al. 2007; Otterbacher 2009; Tsur and Rappoport 2009; Lu et al. 2010; quence of the actual textual quality of the post, but is also affected by…”
Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach (2011, methodology): “Except for the definition in [20], other definitions require viewers' voting on whether they feel a specific review is helpful or not.”
To Buy or to Read: How a Platform Shapes Reviewing Behavior (2021, background): “However, the default sorting of reviews according to existing votes can lead to a “winner circle” bias (Liu et al. 2007).”
Challenges in Sentiment Analysis (2017, background): “However, there is interesting work in sentiment analysis of documents to generate text summaries (Ku, Liang, & Chen, 2006; Liu, Cao, Lin, Huang, & Zhou, 2007; Somprasertsri & Lalitrojwong, 2010; Stoyanov & Cardie, 2006; Lloret, Balahur, Palomar, & Montoyo, ), as well as detecting the patterns of…”
Predicting the “helpfulness” of online consumer reviews (2017, background): “Liu, Cao, Lin, Huang, & Zhou (2007) consider the product review helpfulness problem as a binary classification problem.”
Finding unusual review patterns using unexpected rules (2010, background): “Some researchers also study the helpfulness of reviews [7, 12], but review spam is a different concept.”
Stimulating Online Reviews by Combining Financial Incentives and Social Norms (2017, unknown): “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).”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, background): “From a business perspective, consumer product reviews are most influential if they affect product sales and the online behavior of users of the word-of-mouth forum.”
Semi-Supervised Recognition of Sarcasm in Twitter and Amazon (2010, background): “These ratios are to be expected, since non-sarcastic sentences out-number sarcastic ones, deﬁnitely when most on-line reviews are positive (Liu et al., 2007).”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “A related problem that has also been studied in the past few years is the determination of the usefulness, helpfulness or utility of each review [26, 50, 61, 118, 64, 70, 117].”