OpinionMiner: a novel machine learning system for web opinion mining and extraction

By Wei Jin, Hung Hay Ho, Rohini K. Srihari. Knowledge Discovery and Data Mining, 2009

333 citations recorded by the source corpus.

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Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid (2010, methodology): “Previous methods using frequent itemset mining (Hu and Liu, 2004) or supervised learning (Jin and Ho, 2009; Jin et al., 2009; Wu et al., 2009) have the limitation that they do not group semantically related aspect expressions together.”
Recommender systems based on user reviews: the state of the art (2015, methodology): “The typical approaches to feature extraction include statistics based methods, such as one that captures frequently occurring nouns/phrases as feature candidates through association rule mining (Hu and Liu 2004b), a LDA or SVM based method for identifying aspects directly, or a machine learning meth”
Survey on Aspect-Level Sentiment Analysis (2016, methodology): “(2009) [70] camera reviews binary aspect extraction F1: 78.”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “, Conditional Random Fields (CRF) [33, 52], and Hidden Markov Models (HMM) [22, 34, 35], and sequential rule mining [60].”