Mining the peanut gallery: opinion extraction and semantic classification of product reviews

By Kushal Dave, Steve Lawrence, David M. Pennock. The Web Conference, 2003

2,479 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/338a891907dce447da9a0fa2f27221bd35164163

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Extracting Product Features and Opinions from Reviews (2005, methodology): “Other systems (Morinaga et al., 2002; Kushal et al., 2003) also look at Web product reviews but they do not extract”
A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “The research about the opinion mining began from the early 2000, but the phrase “opinion mining” was firstly used in Dave et al. (2003) (Liu 2012).”
Twitter as a Corpus for Sentiment Analysis and Opinion Mining (2010, result): “(Dave et al., 2003) have obtained contrary results: bigrams and trigrams worked better for the product-review polarity classification.”
What makes a great MOOC? An interdisciplinary analysis of student retention in online courses (2013, background): “The most common techniques search for statistical patterns and trends in the text in order to distinguish between positive and negative reviews (Dave et al. 2003) or use classifiers that determine whether a particular feature or concept is implicitly discussed (Ghani et al. 2006).”
Web Data Extraction, Applications and Techniques: A Survey (2012, background): “[Dave et al. 2003] approached the problem of opinion extraction and subsequent semantic classification of reviews of products.”
Multiple Aspect Ranking Using the Good Grief Algorithm (2007, methodology): “Sentiment Classification Traditionally, categorization of opinion texts has been cast as a binary classification task (Pang et al., 2002; Turney, 2002; Yu and Hatzivassiloglou, 2003; Dave et al., 2003).”
Deriving the Pricing Power of Product Features by Mining Consumer Reviews (2011, background): “for example, words and phrases that appear frequently in the reviews (Dave et al. 2003).”
Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees (2005, background): “Dave et al. [ 6 ] used machine learning methods to classify reviews on several kinds of products.”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, methodology): “For each review, we retrieve the actual textual content of the review and the review rating of the product given by the reviewer.”
A survey on sentiment detection of reviews (2009, methodology): “It seems necessary using more sophisticated techniques to determine the focus of each sentence, so that one can decide whether the author is talking about the topic ( Dave, Lawrence, & Pennock, 2003; Fei, Liu, & Wu, 2004; Kim & Hovy, 2004; Ku, Wu, Lee, & Chen, 2005; Mei, Ling, Wondra, Su, & Zhai, 2”
LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval (2007, methodology): “These include document retrieval [3], collaborative filtering [14], key term extraction [8], definition finding [32], important email routing [4], sentiment analysis [26], product rating [9], and anti web spam [13].”
A holistic lexicon-based approach to opinion mining (2008, background): “Representative works on classification at the document level include [4, 5, 9, 12, 26, 27, 29, 32].”
Author’s Sentiment Prediction (2020, background): “Sentiment analysis solutions have covered a wide range of domains including movie reviews (Pang et al., 2008; Singh et al., 2013; Socher et al., 2011), product reviews (Dave et al., 2003; Turney, 2002; Fang and Zhan, 2015), and social media (Abbasi et al., 2008; Pak and Paroubek, 2010).”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “In addition to semantic features, syntactic features are most commonly applied, particularly word n-grams and part-of-speech n-grams (Dave et al., 2003; Gamon, 2004; Pang et al., 2002).”