A model of textual affect sensing using real-world knowledge

By Hugo Liu, Henry Lieberman, Ted Selker. International Conference on Intelligent User Interfaces, 2003

639 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/de00b6d04d7d4c7d554b404f54b4362c9ba3df92

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Citations in the corpus

Mining the peanut gallery: opinion extraction and semantic classification of product reviews (2003, methodology): “[10] used relationships from the Open Mind Commonsense database and manually-specified ground truth to assign scalar affect values to linguistic features.”
WordNet Affect: an Affective Extension of WordNet (2004, background): “A new interesting approach, corpus-based, is that of (Liu et al., 2003).”
Multi-Dimensional Sentiment Analysis with Learned Representations (2012, background): “, 2006), but also rich multidimensional category sets modeled on those of cognitive psychology (Liu et al., 2003; Alm et al., 2005; Wiebe et al., 2005; Neviarouskaya et al., 2010).”
Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications (2011, methodology): “There are other examples of the strong interest in affective NLP or affective interfacing (Liu et al, 2003; Holzman and Pottenger, 2003; Francisco and Gerv´as, 2006; Kalra and Karahalios, 2005; G´en´ereux and Evans, 2006; Mihalcea and Liu, 2006).”
#Emotional Tweets (2012, background): “Liu et al. (2003) and Mohammad and Yang (2011) worked on email data.”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, methodology): “A number of approaches exists for this analysis: keyword spotting [60, 45], rule-based modelling [115], Semantic Trees [251], Latent Semantic Analysis [82], Transformation-based Learning [243], World-knowledge-Modelling [116], Key-Phrase-Spotting [189], String Kernels [174], and Bayesian Networks [3”
Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews (2006, background): “There is a large body of work on classifying the polarity of a document (e.g., Pang et al. (2002), Turney (2002)), a sentence (e.g., Liu et al. (2003), Yu and Hatzivassiloglou (2003), Kim and Hovy (2004), Gamon et al. (2005)), a phrase (e.g., Wilson et al. (2005)), and a specific object (such as a…”
Emotions from Text: Machine Learning for Text-based Emotion Prediction (2005, background): “…of subjective clauses (Wilson, Wiebe and Hwa, 2004), determining word polarity (Hatzivassiloglou and McKeown, 1997) or texts’ attitudinal valence, e.g. (Turney, 2002), (Bai, Padman and Airoldi, 2004), (Beineke, Hastie and Vaithyanathan, 2003), (Mullen and Collier, 2003), (Pang and Lee, 2003).”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Perhaps the most complex approach to textual affect sensing involves systems that construct affective models from large corpora of world knowledge and applying these models to identify the affective tone in texts [14], [149], [150], [151], [152].”
A survey on sentiment detection of reviews (2009, neutral): “... SchAutze, 1997; Spertus, 1997), but only in the early 2000s did it become a major subfield of the information management discipline (Chaovalit & Zhou, 2005; Dimitrova, Finn, Kushmerick, & Smyth, 2002; Durbin, Neal Richter, & Warner, 2003; Efron, 2004; Gamon, 2004; Glance, Hurst, & Tomokiyo, 2004”
Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis (2004, background): “…on semantic resources, such as a sentiment or affect lexicon (Nasukawa and Yi 2003, Subasic and Huettner 2001), or a large scale knowledge base (Liu et al 2003) on the one hand, and approaches that try to learn patterns directly from tagged data, without additional resources (Dave et al 2003,…”
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales (2005, background): “We hope that some of the insights derived here might apply to other scales for text classifcation that have been considered, such as clause-level opinion strength (Wilson, Wiebe, and Hwa, 2004); affect types like disgust (Subasic and Huettner, 2001; Liu, Lieberman, and Selker, 2003); reading level…”
A machine learning approach to sentiment analysis in multilingual Web texts (2009, methodology): “…some cases other types of sentiment classiﬁcations are used (e.g., the emotions “happy, sad, anger, fear, disgust, surprise”), which reveal other aspects of the content (Huber et al. 2000, Turney 2002, Kamps and Marx 2002, Liu et al. 2003, Nijholt 2003, Whitelaw et al. 2005, Leshed and Kaye 2006).”
Identifying Expressions of Emotion in Text (2007, background): “[4] have utilized real-world knowledge about affect drawn from a common-sense knowledge base.”
ConceptNet — A Practical Commonsense Reasoning Tool-Kit (2004, methodology): “The algorithm is a simplification of Liu et al’s Emotus Ponens system [ 24 ].”