Pulse: Mining Customer Opinions from Free Text

By Michael Gamon, Anthony Aue, Simon Corston-Oliver, Eric K. Ringger. International Symposium on Intelligent Data Analysis, 2005

519 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b61edf6d800e8e4f9285f0fb3707938ce9c03661

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In Artificial Intelligence

Citations in the corpus

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].”
Bibliometrics to webometrics (2008, methodology): “In order to address issues like these, new software has been developed by large companies like IBM’s Web Fountain [96] and Microsoft’s Pulse [97].”
Mining Opinions in Comparative Sentences (2008, background): “Sentiment classification at the sentence-level has also been studied (e.g., Riloff and Wiebe 2003; Kim and Hovy 2004; Wilson et al 2004; Gamon et al 2005; Stoyanov and Cardie 2006).”
Sentiment Analysis and Subjectivity (2010, methodology): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
Modeling online reviews with multi-grain topic models (2008, background): “[12] present an unsupervised system that”
Opinion Mining and Sentiment Analysis (2008, background): “[104], depicting (automatically determined) topics discussed in reviews of the Volkswagen Golf.”
Movie review mining and summarization (2006, methodology): “In the Pulse system introduced by Gamon et al [4], a bootstrapping process was used to train a sentiment classi.er.”
Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states (2008, background): “Hatzivassiloglou (2003), Nasukawa and Yi (2003), Kim and Hovy (2004), Hu and Liu (2004), Kudo and Matsumoto (2004), Popescu and Etzioni (2005), and Gamon et al. (2005) have all worked on identifying sentences expressing positive and negative sentiments.”
A systematic analysis of performance measures for classification tasks (2009, methodology): “Additional performance measures other than the above are Accuracy used in Pang et al. (2002) and Thomas et al. (2006), or the correspondence between Precision and Recall in Gamon, Aue, Corston-Oliver, and Ringger (2005) .”
Inferring Networks of Substitutable and Complementary Products (2015, background): “A variety of works have used topic models within recommender systems ( e.g. [6, 10, 11, 22, 23, 28, 31, 32]), though generally with the goal of predicting user ratings (or opinions) rather than learning relationships between products as we do here.”
Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews (2006, background): “…of a document (e.g., Pang et al. (2002), Turney (2002)), a sentence (e.g., Liu et al. (2003), Yu and Hatzivassiloglou (2003), Kim and Hovy (2004), Gamon et al. (2005)), a phrase (e.g., Wilson et al. (2005)), and a specific object (such as a product) mentioned in a document (e.g., Morinaga et al.…”
Sentiment in Twitter events (2011, background): “While some identify the objects discussed and the polarity (positive, negative, or neutral) of sentiment expressed about them (Gamon et al., 2005), other algorithms assign an overall polarity to a text, such as a movie review (Pang & Lee, 2004).”
Building a Sentiment Summarizer for Local Service Reviews (2008, unknown): “1e.g., tripadvisor.com or yelp.com 2e.g., maps.google.com, local.yahoo.com or maps.live.com/localsearch”
Learning Attitudes and Attributes from Multi-aspect Reviews (2012, background): “Matrix factorization techniques [18] use nothing but numeric scores to learn latent ‘aspects’ that best explain users’ preferences, while clustering and topic-modeling approaches use nothing but review text to model ‘aspects’ in terms of words that appear in document corpora [4], [9], [11].”
A Joint Model of Text and Aspect Ratings for Sentiment Summarization (2008, background): “Text excerpts are usually extracted through string matching (Hu and Liu, 2004a; Popescu and Etzioni, 2005), sentence clustering (Gamon et al., 2005), or through topic models (Mei et al., 2007; Titov and McDonald, 2008).”