WordNet Affect: an Affective Extension of WordNet

By Carlo Strapparava, Alessandro Valitutti. International Conference on Language Resources and Evaluation, 2004

1,735 citations recorded by the source corpus.

In this paper we present a linguistic resource for the lexical representation of affective knowledge. This resource (named WORDNETAFFECT) was developed starting from WORDNET, through a selection and tagging of a subset of synsets representing the affective

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

View Paper: https://www.semanticscholar.org/paper/52b3d9fcc8d0d3beb0357c598358f0435784d517

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Citations in the corpus

AffectiveSpace: Blending Common Sense and Affective Knowledge to Perform Emotive Reasoning (2009, background): “WordNet-Affect [12] is a linguistic resource for the lexical representation of affective knowledge, developed starting from WordNet [13].”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “SenticNet 1.0 was further extended by Poria et al. [23] with emotion label taken from WNA [4].”
Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text (2014, background): “NetAffect [57] to mark the presence of emotion-bearing words in text or Affective Norms for English Words”
Understanding the Effect of Gender and Stance in Opinion Expression in Debates on “Abortion” (2018, methodology): “We used WordnetAffect (Valitutti, 2004) to find affective concepts correlated with affective words and compare the affective concepts across these different groups.”
Beyond Sentiment: The Manifold of Human Emotions (2012, background): “The work described in (Strapparava and Valitutti, 2004; Quan and Ren, 2009; Mohammad and Turney, 2011) address the task of constructing a useful corpus for emotion analysis.”
A review on sentiment analysis and emotion detection from text (2021, background): “Batbaatar et al. (2019) (Strapparava et al. 2004) and NRC (Mohammad and Turney 2013) word–emotion lexicons.”
Zooming in on Gender Differences in Social Media (2016, methodology): “WA (Strapparava and Valitutti, 2004) is a resource that was created starting with WordNet (Miller et al., 1993), by annotating synsets with several emotions.”
Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online (2013, methodology): “It would also be useful to compare the relatively simple techniques used in this analysis against other platforms and tools used for the sentiment analysis and opinion mining process, for example WordNet Affect [29] and SentiWordNet [30,31].”
Natural language processing in mental health applications using non-clinical texts† (2017, methodology): “1990), particularly its extension WordNet-affect (Strapparava and Valitutti, 2004) and normative databases such as the Affective Norm for English Words (ANEW)”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, methodology): “Some of these studies rely on affect lexicons, such as LIWC (Pennebaker et al., 2007) and WordNet Affect (Strapparava et al., 2004), to extract emotion features from a text-based corpus.”
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, background): “…(Cambria and Hussain, 2015) is an affective commonsense knowledge base built upon Con-ceptNet (Speer and Havasi, 2012), the graph representation of the Open Mind corpus, and WordNet-Affect (Strapparava and Valitutti, 2004), a linguistic resource for the lexical representation of affect (Fig.”
SemEval-2007 Task 14: Affective Text (2007, methodology): “We provided a set words extracted from WordNet Affect (Strapparava and Valitutti, 2004), relevant to the six emotions of interest.”
Emotion Analysis as a Regression Problem - Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation (2016, background): “It contains both, sentiment assessments (positive, negative, neutral and ambiguous) and a hierarchy of various emotion categories [36, 37].”
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances (2019, background): “A number of emotion lexicons [7], [8] have been developed to tackle the textual”
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text (2019, methodology): “To that end, several lexical resources have been created, such as WordNet-Affect (Strapparava et al., 2004) and SentiWordNet (Esuli and Se-bastiani, 2007).”