A Joint Model of Text and Aspect Ratings for Sentiment Summarization

By Ivan Titov, Ryan T. McDonald. Annual Meeting of the Association for Computational Linguistics, 2008

725 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8c8bf1efdbdbe37bd854192a7ba0716498e1802b

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Citations in the corpus

Rated aspect summarization of short comments (2009, background): “[17] I. Titov and R. McDonald.”
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].”
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (2014, methodology): “The work of [20] uses LDA based model to identify ‘topics’ that are correlated with user ratings.”
An Unsupervised Aspect-Sentiment Model for Online Reviews (2010, methodology): “This approach overcomes the problems of frequent-term methods, as well as the issues raised by Titov and McDonald (2008b).”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “Titov and McDonald [98] proposed a statistical model that is able to discover aspects from text and to extract textual evidence from reviews supporting each aspect rating.”
Co-Training for Cross-Lingual Sentiment Classification (2009, background): “In recent years, sentiment classification has drawn much attention in the NLP field and it has many useful applications, such as opinion mining and summarization (Liu et al., 2005; Ku et al., 2006; Titov and McDonald, 2008).”
Joint sentiment/topic model for sentiment analysis (2009, methodology): “Being aware of the limitation that MG-LDA is still purely topic based without considering the associations between topics and sentiments, Titov and McDonald further proposed the Multi-Aspect Sentiment model (MAS) [19] by extending the MG-LDA framework.”
Improving the quality of predictions using textual information in online user reviews (2013, background): “Review processing has focused on identifying sentiments, product features [9] or a combination of both [16,1,34].”
Ratings meet reviews, a combined approach to recommend (2014, background): “In [8, 29, 30], the authors also considered the interpretable aspects to make better predictions.”
Sentiment Analysis and Subjectivity (2010, background): “Other related works on feature extraction mainly use the ideas of topic modeling and clustering to capture topics/features in reviews [58, 59, 62, 89, 93, 106].”
Syntactic Topic Models (2008, background): “Topic models are widely applied to text despite a willful ignorance of the underlying linguistic structures that exist in natural language.”
SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews (2015, methodology): “On the other hand, several proposals [13], [18], [19], [7] on opinion mining aim at identifying latent topical-aspects and predicting ratings/sentiments on identified aspects in product reviews.”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, background): “In recent years, sentiment classiﬁcation has drawn much attention in the natural language processing (NLP) ﬁeld and it has many useful applications, such as opinion mining and summarization (Liu, Hu, and Cheng 2005; Ku, Liang, and Chen 2006; Titov and McDonald 2008).”
SemEval-2014 Task 4: Aspect Based Sentiment Analysis (2014, background): “ABSA is critical in mining and summarizing opinions from on-line reviews (Gamon et al., 2005; Titov and McDonald, 2008; Hu and Liu, 2004a; Popescu and Etzioni, 2005).”
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).”
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.”
Feature-based transfer learning with real-world applications (2010, background): “In sentiment analysis, there are some interesting tasks, for example, opinion extraction and summarization [81, 118, 184, 93], opinion integration [117], review spam identification [88], review quality prediction [116, 114] and opinion analysis in multiple languages [1], etc.”