Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon

By Saif M. Mohammad, Peter D. Turney. HLT-NAACL 2010, 2010

1,049 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3811ba132e669b1d94938df0636897ce957cc170

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Citations in the corpus

Where is Your Evidence: Improving Fact-checking by Justification Modeling (2018, methodology): “…and positivity of a statement on a scale of 1-to-5 (Thelwall et al., 2010); emotion using the NRC Emotion Lexi-con (EmoLex), which associates each word with eight basic emotions (Mohammad and Turney, 2010), and the Linguistic Inquiry and Word Count (LIWC) lexicon (Pennebaker et al., 2001).”
Lexicon-Based Methods for Sentiment Analysis (2011, background): “…resource among computational linguists, and has been used in sentiment, emotion, and subjectivity tasks (Akkaya et al. 2010; Mellebeek et al. 2010; Mohammad and Turney 2010; Yano, Resnik, and Smith 2010), although there are mixed reports on its reliability (Snow et al. 2008; Callison-Burch 2009;…”
User-Level Race and Ethnicity Predictors from Twitter Text (2018, unknown): “We also used the word lexicon derived using crowd-sourcing from (Mohammad and Turney, 2010; Mohammad and Turney, 2013) and found it had slightly worse predictive results.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “NRC-EIL provides intensity scores for the words and emotions in the NRC Emotion Lexicon.”
How Translation Alters Sentiment (2016, methodology): “The sentiment lexicon features are derived from existing, general-purpose, manual lexicons, namely NRC Emotion Lexicon (Mohammad & Turney, 2010, 2013), Bing Liu Lexicon (Hu & Liu, 2004), and MPQA Subjectivity Lexicon (Wilson et al., 2005).”
Sentiment Analysis of Short Informal Texts (2014, methodology): “Again, we use scores +1/-1 for the NRC Emotion Lexicon and Bing Liu’s Lexicon and scores +1/-1 for weak associations and +2/-2 for strong associations in the MPQA Subjectivity Lexicon.”
Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak (2020, unknown): “The NRC emotion lexicon was used to calculate the presence of eight basic emotions (“anger”, “ fear”, “anticipation”, “trust”, “surprise”, “sadness”, “joy”, and “disgust”) and their corresponding valence in coronavirus news headlines (Mohammad and Turney, 2010).”