Learning to identify emotions in text

By Carlo Strapparava, Rada Mihalcea. 2008

742 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Harnessing Twitter "Big Data" for Automatic Emotion Identification (2012, background): “The approaches in [8], [13], [19] collect blog posts that have been assigned mood labels (e.”
User-Level Race and Ethnicity Predictors from Twitter Text (2018, positive): “The most popular model of discrete emotions is the Ekman model (Ekman, 1992; Strapparava and Mihalcea, 2008; Strapparava et al., 2004) which posits the existence of six basic”
Beyond Binary Labels: Political Ideology Prediction of Twitter Users (2017, background): “The most studied model of discrete emotions is the Ekman model (Ekman, 1992; Strapparava and Mihalcea, 2008; Strapparava et al., 2004) which posits the existence of”
Beyond Sentiment: The Manifold of Human Emotions (2012, methodology): “The work described in (Rubin et al., 2004; Strapparava and Mihalcea, 2008) classiﬁed data us-ing a categorical model suggested by psychological literature.”
The Hourglass of Emotions (2011, background): “[43] WN presence, LSA 6 News stories WNA”
Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak (2020, methodology): “By using the Lexicon method recommended by Saif (Mohammad and Turney, 2013) in which words are associated with emotions, sentiments and opinion categories (Wilson et al., 2005; Strapparava and Mihalcea, 2008; Strapparava et al., 2006).”
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys (2010, background): “Strapparava and Mihalcea (2008) classify blog posts and news headlines to six sentiment categories.”
Text‐based emotion detection: Advances, challenges, and opportunities (2020, background): “12 This article aims to comprehensively guide novices in the field of text-based ED thus this article presents major-related concepts, detection approaches, and emotion-labeled data sources available for undertaking text-based ED operations.”
Emotion Analysis as a Regression Problem - Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation (2016, methodology): “NB-BLOG [35], the only machine learning approach among the BE systems, uses a Naive Bayes classifier.”
Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger in News Content (2015, background): “For instance, for both annotations for emotions in blogs (Aman and Szpakowicz 2007) and for news headlines (Strapparava and Mihalcea 2008), fear was more reliably coded than anger.”
Third wave positive psychology: broadening towards complexity (2020, background): “…of power differentials on community engagement (Danescu-Niculescu-Mizil et al., 2013); personal values (Boyd et al., 2015); emotions (Bollen et al., 2011; Strapparava & Mihalcea, 2008); and psychological traits (Guntuku et al., 2017; Mitchell et al., 2015; Park et al., 2015; Sagi & Dehghani, 2014).”
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text (2019, neutral): “, 2011), (Strapparava and Mihalcea, 2008), (Sykora et al.”