Sentiment analysis of blogs by combining lexical knowledge with text classification

By Prem Melville, Wojciech Gryc, Richard D. Lawrence. Knowledge Discovery and Data Mining, 2009

587 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/510bd55446eb3d3d596b5262d18f45886dae3a9b

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In Artificial Intelligence

Citations in the corpus

From Theories to Queries (2011, methodology): “Mann and McCallum (2010) describe a semi-supervised method of combining such constraints with unlabeled data in exponential models, and Melville et al. (2009) combine this domain knowledge with labeled examples for näıve Bayes by pooling multinomials.”
A survey of multimodal sentiment analysis (2017, background): “Political parties are interested in opinion polling to gauge voting intentions [3].”
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, background): “Melville et al. (Melville et al., 2009) developed a framework that exploits word-class association information for domain-dependent sentiment analysis.”
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr (2012, background): “These include active learning strategies [12, 8] and general classifier induction algorithms [6, 9].”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “Based on Melville et al. [2009], labeling political posts is much more difficult than that of product reviews, because posts are more emotional, mostly mentioning implicit comments and judgments about the candidate, and may apply cultural references to make a point.”
Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria (2009, methodology): “The derivation and details of these models are not directly relevant to this paper, but can be found in (Melville et al., 2009”
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys (2010, background): “Others combine additional feature types for this decision (Yu and Hatzivassiloglou, 2003; Kim and Hovy, 2004; Wilson et al., 2005; Bloom et al., 2007; McDonald et al., 2007; Titov and McDonald, 2008a; Melville et al., 2009).”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “[116] developed a framework for domain-dependent sentiment analysis using lexical association information.”
Semantic Text Matching for Long-Form Documents (2019, background): “like web search [7] use short source queries while targeting longform documents; on the lower left, short text retrieval tasks like Twitter search [9, 41] use short source queries and target short documents; on the lower right, the tasks of document classification like sentiment analysis aim to cate”
The Constrained Weight Space SVM: Learning with Ranked Features (2011, methodology): “For technical details of their lexical model, see (Melville et al., 2009).”
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings (2018, background): “Melville et al. (Melville, Gryc, and Lawrence 2009) developed a framework that exploits word-class association information for domain-dependent sentiment analysis.”