Structured Models for Fine-to-Coarse Sentiment Analysis

By Ryan T. McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, Jeffrey C. Reynar. Annual Meeting of the Association for Computational Linguistics, 2007

336 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dea301ddc616ed7da7568b91a6627699fcbd07cd

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Citations in the corpus

Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis (2008, background): “Another approach encodes frequent sub-sentential patterns (e.g., McDonald et al. (2007)) as features; these might indirectly capture some of the subsentential interactions that affect polarity.”
Building a Sentiment Summarizer for Local Service Reviews (2008, methodology): “The model we employ for sentiment classification is a hybrid that uses both lexicon-based and machine learning algorithms.”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, unknown): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
Author’s Sentiment Prediction (2020, background): “McDonald et al. (2007) showed the effectiveness of sentence level prediction on document level classification.”
Detecting Hate Speech on the World Wide Web (2012, background): “McDonald (McDonald et al, 2007) has reported some success mixing fine and course labeling in sentiment analysis.”
Sentiment Analysis and Subjectivity (2010, background): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
Structure-Aware Review Mining and Summarization (2010, methodology): “For example, with CRFs, Zhao et al (2008) and McDonald et al. (2007) performed sentiment classification in sentence and document level; Breck et al (2007) identified opinion expressions from newswire documents; Choi et al. (2005) determined opi-654 nion holders to opinions also from newswire data.”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, background): “More recently, McDonald et al. (2007) investigate a structured model for jointly classifying the sentiment of a text at varying levels of granularity.”
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys (2010, background): “Moreover, the assigned tag applies to the whole blog post while a ﬁner grained sentiment extraction is needed (McDonald et al., 2007).”
Multi-Level Structured Models for Document-Level Sentiment Classification (2010, methodology): “Alternative approaches include explicitly accounting for this structure by treating subjective sentence extraction as a sequence-labeling problem, such as in McDonald et al. (2007).”
Opinion Mining and Sentiment Analysis (2008, background): “[205] leverage relationships between labels assigned at different classification stages, such as the word level or sentence level, finding that a “fine-to-coarse” categorization procedure is an effective strategy.”
Weakly Supervised Joint Sentiment-Topic Detection from Text (2012, background): “All the aforementioned work shares some similar limitations: (1) they only focused on sentiment classification alone without considering the mixture of topics in the text, which limits the effectiveness of the mining results to users; (2) most of the approaches [3], [4], [7], [15] favor supervised l”
Co-Training for Cross-Lingual Sentiment Classification (2009, background): “Most recently, McDonald et al. (2007) investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity.”
Joint sentiment/topic model for sentiment analysis (2009, background): “…of topics in the text, which is less informative to users and may limit the usefulness of the results; (2) most of the approaches [16, 15, 4, 3, 13, 25] are favored in supervised learning, which require a labeled corpus for training and potentially restrain their applicability to other domains…”
MPQA 3.0: An Entity/Event-Level Sentiment Corpus (2015, background): “There is increasing interest in more ﬁne-grained levels - sentence-level (Yu and Hatzivassiloglou, 2003; McDonald et al., 2007), phrase-level (Choi and Cardie, 2008), aspect-level (Hu and Liu, 2004; Titov and McDonald, 2008), etc.”