Using Hashtags to Capture Fine Emotion Categories from Tweets

By Saif M. Mohammad, Svetlana Kiritchenko. International Conference on Climate Informatics, 2015

406 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5a0024685d31416a478df97bc665ae7a3511516f

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Citations in the corpus

Dynamics of information diffusion and its applications on complex networks (2016, background): “And a user’s interest feature [171] and emotion categories [172] could also be extracted from her/his information content, or even the posted information of the users that s/he followed in the social networks.”
#Communing affiliation: Social tagging as a resource for aligning around values in social media (2017, background): “Discourse Context Media (2017), http://dx.doi.org/10.1016/j.dcm.2017.08.001 using tags to improve natural language processing tasks such as identifying and categorising sentiment (Davidov et al., 2010; Kunneman et al., 2015; Mohammad and Kiritchenko, 2015).”
EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks (2017, background): “While the proliferation of social media has made it possible for us to acquire large datasets with implicit labels in the form of hashtags (Mohammad and Kiritchenko, 2015), such labels are noisy and reliable.”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, methodology): “We bootstrap a set of noisy labels to obtain large-scale emotion tweets, and then perform annotation via distant supervision as in (Go et al., 2009; González-Ibánez et al., 2011; Wang et al., 2012; Mohammad and Kiritchenko, 2015; Abdul-Mageed and Ungar, 2017).”
Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design (2019, background): “6 Hashtag The occurrence of hashtags in hashtag lexicon [22].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “Keywords: sentiment analysis, emotions, artificial intelligence, machine learning, natural language processing (NLP), social media, emotion lexicons, fairness in NLP”
Sentiment Analysis of Short Informal Texts (2014, background): “…analysis system within larger systems for detecting intentions behind political tweets (Mohammad, Kiritchenko, & Martin, 2013), for detecting emotions in text (Mohammad & Kiritchenko, 2014), and for detecting sentiment towards particular aspects of target entities (Kiritchenko et al., 2014).”
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words (2018, background): “is growing work on automatically determining word–sentiment and word–emotion associations (Yang et al., 2007; Mohammad and Kiritchenko, 2015; Yu et al., 2015; Staiano and Guerini, 2014).”
The spread of true and false news online (2018, background): “The second dataset contains a list of 32,389 Twitter hashtags and their weighted associations with the same emotions [33] [63].”