Multi-Level Structured Models for Document-Level Sentiment Classification

By Ainur Yessenalina, Yisong Yue, Claire Cardie. 2010

212 citations recorded by the source corpus.

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Citations in the corpus

Coarse-to-Fine Question Answering for Long Documents (2016, methodology): “Treating sentence selection as a latent variable has been explored in classiﬁ-cation (Yessenalina et al., 2010; Lei et al., 2016), however, to our knowledge, has not been applied for question answering.”
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014, background): “Review- [24, 41] and sentence-level [39, 21] analysis attempt to classify the sentiment of a whole review or sentence to one of the predefined sentiment polarities, including positive, negative and sometimes neutral, while phrase-level analysis [40, 18, 6] attempts to extract explicit features of th”
Stance Classification using Dialogic Properties of Persuasion (2012, background): “…are in doubt by both lay and acedamia, then organic chemistry, physics and gravity are in doubt. bates (Thomas et al., 2006; Bansal et al., 2008; Yessenalina et al., 2010; Balahur et al., 2009; Bur-foot et al., 2011); (2) company-internal discussion sites (Murakami and Raymond, 2010; Agrawal et…”
Bridging CNNs, RNNs, and Weighted Finite-State Machines (2018, background): “Other works jointly learned to encode text and extract the span which best explains the model’s prediction (Yessenalina et al., 2010; Lei et al., 2016).”
Linguistic Structured Sparsity in Text Categorization (2014, background): “For example, for the vote sentiment analysis datasets, latent variable models of Yessenalina et al. (2010) achieved a superior result of 77.67%.”
Multilingual Hierarchical Attention Networks for Document Classification (2017, background): “For text classification, studies which aimed to learn the importance of sentences included those by Yessenalina et al. (2010); Pappas and Popescu-Belis (2014); Yang et al. (2016) and more recently those by Pappas and Popescu-Belis (2017); Ji and Smith (2017).”
Sentiment Analysis and Opinion Mining (2012, methodology): “In Yessenalina et al. (2010), multi-level structured models were proposed.”
Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains (2019, methodology): “Earlier techniques use hand-crafted features and external resources [21,23,38,43,46], which involve a great deal of human efforts.”
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers (2014, methodology): “The log-odds score is defined here as the log of the model probability of the class label for an instance (document) 11For the movie and vote sentiment analysis datasets, latent variable models in Yessenalina et al. (2010) achieved the best results of 92.50 and 77.67, respectively.”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “This level is associated with the task called document-level sentiment classification [104,58,18,31,108,102,66,41].”