Latent aspect rating analysis on review text data: a rating regression approach

By Hongning Wang, Yue Lu, Chengxiang Zhai. 2010

747 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Towards Coherent and Cohesive Long-form Text Generation (2018, methodology): “We use the TripAdvisor hotel English reviews dataset collected by Wang et al. (2010) and the Yelp English reviews dataset 3 .”
Learning to Write with Cooperative Discriminators (2018, background): “The first is the TripAdvisor corpus (Wang et al., 2010),”
Lifelong Learning CRF for Supervised Aspect Extraction (2017, background): “…2010; Qiu et al., 2011; Poria et al., 2014), topic modeling (Mei et al., 2007; Titov and McDonald, 2008; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Moghaddam and Ester, 2011; Mukherjee and Liu, 2012; Lin and He, 2009; Zhao et al., 2010; Jo and Oh, 2011; Fang and Huang, 2012;…”
Joint Deep Modeling of Users and Items Using Reviews for Recommendation (2017, background): “Recently, some studies [17] [16] have shown that using review text can improve the prediction accuracy of recommender systems, in particular for the items and users with few ratings [34].”
A Game Theoretic Approach to Class-wise Selective Rationalization (2019, methodology): “We also evaluate algorithms on the multi-aspect beer [23] and hotel reviews [29] that are commonly used in the ﬁeld of rationalization [8, 19].”
Hidden factors and hidden topics: understanding rating dimensions with review text (2013, background): “The most similar line of work is perhaps that of aspect discovery [6, 25, 29].”
SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews (2015, background): “[20] build a regression model to capture the aspect ratings from the overall ratings.”
Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, background): “…in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester, 2011; Sauper et al., 2011; Mukherjee and Liu, 2012; He et al.,…”
Deriving Machine Attention from Human Rationales (2018, methodology): “ics of the beer review dataset. Domain transfer Our second experiment focuses on domain transfer from beer reviews to different aspects of hotel reviews. We use the TripAdvisor4 hotel review dataset (Wang et al., 2010), with the following three aspects as our transfer target: location, cleanliness, ”
Automatic construction of a context-aware sentiment lexicon: an optimization approach (2011, methodology): “Yet another kind of approaches exploit the association between words and expression-level or document-level sentiment [2, 12, 20].”
Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “, 2012) employed seed words to guide topic models to learn topics of specific interest to a user, while (Wang et al., 2010) and (Mukherjee and Liu, 2012) employed seeding words to extract related product aspects from product reviews.”
Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, background): “…al., 2006; Wang and Wang, 2008; Wu et al., 2009; Lin and He, 2009; Zhang et al., 2010; Mei et al., 2007; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Mukherjee and Liu, 2012; Fang and Huang, 2012; Zhou et al., 2013; Liu et al., 2013; Poria et al., 2014), we are not aware of any…”
Sentiment Analysis and Opinion Mining (2012, methodology): “Ma and Wan (2010) used the centering theory and supervised learning. Meng and Wang (2009) extracted aspects from product specifications, which are structured data.”
Aspect Extraction through Semi-Supervised Modeling (2012, background): “, 2008), rating multi-aspects (Wang et al., 2010; Moghaddam and Ester, 2011), summarizing”
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, neutral): “There are also many topic models that have been used for the task of aspect extraction and categorization in, for example, [4, 11, 24, 28, 49, 27, 32, 33, 43, 10, 5, 14, 22, 23, 30, 45, 46].”
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019, background): “ion of this recommendation. We select the predicates among the item features extracted from user-generated reviews. User reviews provide a fine-grained understanding of a user’s evaluation of an item [33]. Feature-level sentiment analysis techniques [12] can be readily applied to reviews toconstruct”
Mining topics in documents: standing on the shoulders of big data (2014, methodology): “Topic models have been used for the task by many researchers [5, 10, 16, 21, 23, 25, 26, 32, 33, 37].”