Automatic construction of a context-aware sentiment lexicon: an optimization approach

By Yue Lu, Malú Castellanos, Umeshwar Dayal, Chengxiang Zhai. 2011

321 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification (2014, methodology): “In the second stage, we extract feature-opinion pairs from the corpus [5, 8], then use the overall sentiment orientations of the reviews as constraints to learn the sentiment polarities of these pairs automatically, using a novel optimization framework.”
Collaborative Multi-domain Sentiment Classification (2015, methodology): “The simplest method to solving this problem is training a domain-specific sentiment classifier or building a domain-specific sentiment lexicon for each single domain independently [27], [20].”
Learning to Rank Features for Recommendation over Multiple Categories (2016, methodology): “In the ﬁrst stage, we construct the set of feature-sentiment pairs from a corpus of textual reviews based on the state-of-art optimization approach described in [4, 18, 19] due to its high accuracy.”
Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation (2015, methodology): “…in the year of 2007 through 2009 [2], because the CFapproaches, especially those based on Matrix Factorization (MF) techniques [28, 8] on user-item numerical rating matrices, achieved important success in the task of rating prediction [12], and also exhibited great advantage in many other…”
Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015, methodology): “Fortunately, the ever developing phrase-level sentiment analysis techniques [18, 10, 36, 29] have made it possible to extract, summarize and structure the rich product features and user preferences automatically from free-text reviews, and have begun to be leveraged for personalized recommendation t”
Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary (2018, background): “The English open source sentiment dictionary has the WordNet [5].”
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019, background): “y s as +1 or -1, to represent positive or negative opinions. As how to construct a sentiment lexicon with phrase-level sentiment analysis is not the focus of this work, we refer interested readers to [12, 39] for more details. The extracted item features become candidate variables for predicate sele”
Explainable recommendation with fusion of aspect information (2018, methodology): “Sentiment-based methods utilize the sentiment analysis [2, 13, 20, 26] for recommendation.”
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis (2016, background): “, 2011; Wang and Ester, 2014), domainspecific and target-dependent sentiment classification (Lu et al., 2011; Ofek et al., 2016; Dong et al., 2014; Tang et al., 2015).”
Unsupervised sentiment analysis with emotional signals (2013, background): “The third is called corpus-based methods [23, 34, 39], which infer sentiment orientation of the words from a given corpus by exploring the relation between the words and some observed seed sentiment words/information, and then build a domain-dependent sentiment lexicon.”
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (2018, methodology): “As the construction of a sentiment lexicon is not a contribution of this work and limited by space, we will not discuss the details of this procedure; interested readers can refer to [21, 38] for more details.”
SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews (2015, background): “Studies on phrase level sentiment [13], [20], [12], [15], [30] use NLP tools to analyze the dependency between the words in a sentence and extract aspect-opinion phrase pairs, e.”
A Survey of Opinion Mining and Sentiment Analysis (2012, unknown): “In [63], Lu et al.”