Latent aspect rating analysis without aspect keyword supervision

By Hongning Wang, Yue Lu, Chengxiang Zhai. Knowledge Discovery and Data Mining, 2011

339 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d4338f021d53fa01fe8d72e4715617f9ce158924

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In Artificial Intelligence

Citations in the corpus

Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis (2016, methodology): “, 2012), aspect rating (Titov and McDonald, 2008; Wang et al., 2011; Wang and Ester, 2014), domainspecific and target-dependent sentiment classification (Lu et al.”
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews (2018, background): “Some works assume that the review is available when predicting the rating score, such as SUIT [22], LARAM [33], and recent DeepCoNN [43].”
MMALFM (2018, positive): “Some works assume that the review is available when predicting the rating score, such as SUIT [39], LARAM [59], and recent DeepCoNN [67].”
FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering (2015, methodology): “This enables our approach to be used to analyze the aspect distributions of the reviews and latent aspect ratings expressed in the reviews as in [22, 23].”
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016, methodology): “We run FRAUDAR on four real-world graphs: Amazon [18], Trip Advisor [29], Epinions [17], and Wiki-vote [17].”
Survey on Aspect-Level Sentiment Analysis (2016, methodology): “(2011) [21] hotel [40] & mp3 player aspect rating prediction MSE: 1.”
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (2014, methodology): “Moreover, there is rich literature analyzing reviews, e.g. using LDA [14, 26, 23] to uncover topics and sentiments.”