Proceedings - Natural Language Processing in a Deep Learning World 2019
DOI: 10.26615/978-954-452-056-4_126
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EmoTag – Towards an Emotion-Based Analysis of Emojis

Abstract: Despite being a fairly recent phenomenon, emojis have quickly become ubiquitous. Besides their extensive use in social media, they are now also invoked in customer surveys and feedback forms. Hence, there is a need for techniques to understand their sentiment and emotion. In this work, we provide a method to quantify the emotional association of basic emotions such as anger, fear, joy, and sadness for a set of emojis. We collect and process a unique corpus of 20 million emoji-centric tweets, such that we can c… Show more

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Cited by 10 publications
(9 citation statements)
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“…Finally, we also inspected semantic associations for particular kinds of emojis. We considered a 300-dimensional word2vec SGNS model trained on the EmoTag (Shoeb et al, 2019) dataset, and generated a set of nearest neighbours for selected target emojis. different skin tone variants of the Clapping Hand emoji.…”
Section: Discussionmentioning
confidence: 99%
“…Finally, we also inspected semantic associations for particular kinds of emojis. We considered a 300-dimensional word2vec SGNS model trained on the EmoTag (Shoeb et al, 2019) dataset, and generated a set of nearest neighbours for selected target emojis. different skin tone variants of the Clapping Hand emoji.…”
Section: Discussionmentioning
confidence: 99%
“…Their heuristic involves training word vector models and then invoking a word-emotion lexicon to obtain average vectors for 8 emotions. Finally, EmoTag (Shoeb et al, 2019) provides interpretable word vectors that describe words in terms of their association with emojis. These vectors were found to be useful for emotion prediction.…”
Section: Background and Related Workmentioning
confidence: 99%
“…For each emoji, we then retrieved an equal number of tweets labeled as being in English. In total, we obtained a set of 20.8 million tweets over a span of one year (Shoeb et al, 2019).…”
Section: Corpus Datamentioning
confidence: 99%
See 1 more Smart Citation
“…Sentiment analysis is among the most prominent forms of natural language processing, with applications such as social media analytics (Rosenthal et al, 2017;Wang et al, 2019;Shoeb et al, 2019), marketing and customer support (Gamon, 2004), as well as recommendation (Yang et al, 2013). Apart from machine learning-driven systems (Pang et al, 2002;Socher et al, 2013;Kalchbrenner et al, 2014, inter alia), which require supervision using labeled training data, there are also lexical resource-driven systems that exploit sentiment lexicons and can be run out-of-the-box without the need for any labeled training data.…”
Section: Introductionmentioning
confidence: 99%