Multiple Aspect Ranking Using the Good Grief Algorithm

By Benjamin Snyder, Regina Barzilay. 2007

341 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

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Citations in the corpus

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, background): “Often, co-occurrence statistics of a word and its context are used to describe each word (Turney and Pantel, 2010; Baroni and Lenci, 2010), such as tf-idf.”
Learning Attitudes and Attributes from Multi-aspect Reviews (2012, methodology): “[29] and [30] used multi-aspect ratings as a means to summarize review corpora.”
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (2011, background): “Applications to many different domains have been presented, ranging from movie reviews (Pang et al., 2002) and congressional floor debates (Thomas et al., 2006) to product recommendations (Snyder and Barzilay, 2007; Blitzer et al., 2007).”
Beyond the Stars: Improving Rating Predictions using Review Text Content (2009, background): “Review processing has focused on identifying sentiment, product features [5, 13, 6, 18] or a combination of both at once [8, 11, 1, 19].”
A Joint Model of Text and Aspect Ratings for Sentiment Summarization (2008, background): “However, it has been observed that ratings for different aspects can be correlated (Snyder and Barzilay, 2007), e.g., very negative opinion about room cleanliness is likely to result not only in a low rating for the aspect rooms , but also is very predictive of low ratings for the aspects service…”
Structure-Aware Review Mining and Summarization (2010, background): “Recently, there are some studies on joint sentiment/topic extraction (Mei et al. 2007; Titov and McDonald, 2008; Snyder and Barzilay, 2007).”
Multi-Dimensional Sentiment Analysis with Learned Representations (2012, background): “After collecting the text and reaction distributions from the Web, we tokenized all documents with at least one reaction.”
Learning Word Vectors for Sentiment Analysis (2011, methodology): “Thus, we extend the model with a supervised sentiment component that is capable of embracing many social and attitudinal aspects of meaning (Wilson et al., 2004; Alm et al., 2005; Andreevskaia and Bergler, 2006; Pang and Lee, 2005; Goldberg and Zhu, 2006; Snyder and Barzilay, 2007).”
A survey of sentiment analysis in social media (2018, background): “…[86] and Snyder et al. [113] expanded the basic task of two-class classiﬁcation as either positive or negative for predicting star ratings on either a 3 or a 4 star scale, while Snyder et al. [113] performed an in-depth review analysis by predicting ratings for various aspects of the given target.”
Deriving the Pricing Power of Product Features by Mining Consumer Reviews (2011, background): “Such heterogeneity stimulated additional research on identifying product features on which consumers expressed their opinions [9, 16– 18, 26, 27].”
Designing novel review ranking systems: predicting the usefulness and impact of reviews (2007, neutral): “view [4, 6, 11, 12, 14, 18–24].”
Opinion Mining and Sentiment Analysis (2008, methodology): “The code used in Snyder and Barzilay [273] is also distributed at the aforementioned URL.”
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions (2011, background): “In multi-aspect rating (Snyder and Barzilay, 2007) one finds several distinct aspects such as food or service in a restaurant and then rates them on a fixed linear scale such as 1-5 stars, where all aspects could obtain just 1 star or all aspects could obtain 5 stars independently.”
Latent aspect rating analysis on review text data: a rating regression approach (2010, background): “For example, Snyder et al. [23] show that modeling the dependencies dependencies among aspects using good grief algorithm can improve the prediction of aspect ratings.”
Bi-Transferring Deep Neural Networks for Domain Adaptation (2016, background): “…et al., 2011b; Chen et al., 2011a; Chen et al., 2012; Li et al., 2013; Xia et al., 2013a) and feature representation adaptation (Thomas et al., 2006; Snyder and Barzilay, 2007; Blitzer et al., 2006; Blitzer et al., 2007; Li et al., 2009; Pan et al., 2010; Zhou et al., 2015a; Zhuang et al., 2015).”
Building a Sentiment Summarizer for Local Service Reviews (2008, methodology): “The model we employ for sentiment classification is a hybrid that uses both lexicon-based and machine learning algorithms.”