What Sparks Joy: The AffectVec Emotion Database

Raji, Shahab; Melo, Gerard de

doi:10.1145/3366423.3380068

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…This opens up further research avenues on possible downstream applications exploiting this knowledge. The most obvious use cases are sentiment analysis (Dong and de Melo, 2018), emotion analysis (Raji and de Melo, 2020), consumer behaviour analytics (Dong et al, 2020), context-sensitive emoji recommendation (Felbo et al, 2017), computational social science and public opinion mining Du et al, 2020), and user modeling , but it may also be useful in dialogue systems (Delobelle and Berendt, 2019), e.g. to detect sarcasm.…”

Section: Discussionmentioning

confidence: 99%

“…Words. The first method we consider is to directly rely on standard distributed word embeddings E (with vocabulary V E ), as these have been shown to carry emotional associations (Raji and de Melo, 2020). Given an emoji e and an emotion (affect) a, we consult E attempting to obtain a vector v e for the emoji as well as a vector v la for the word l a that serves as a label for the affect a (e.g., the words joy, anger, etc.).…”

Section: Distributed Word Vectors Based On Emotionmentioning

confidence: 99%

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EmoTag1200: Understanding the Association between Emojis and Emotions

Shoeb¹,

Melo²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role. One conspicuous aspect of emojis is their use to convey affect in ways that may otherwise be non-trivial to achieve. In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings. We additionally conduct experiments to assess to what extent such associations can be inferred from existing data in an unsupervised manner. Our experiments show that this succeeds when high-quality wordlevel information is available.

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Section: Discussionmentioning

confidence: 99%

Section: Distributed Word Vectors Based On Emotionmentioning

confidence: 99%

EmoTag1200: Understanding the Association between Emojis and Emotions

Shoeb¹,

Melo²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…While we focus on politeness strategies, they are not the only circumstance-sensitive linguistic signals that may be lost or altered during transmission, nor the only type that are subject to individual or culturalspecific perceptions. Other examples commonly observed in communication include, but are not limited to, formality (Rao and Tetreault, 2018) and emotional tones (Chhaya et al, 2018;Raji and de Melo, 2020). As we are provided with more opportunities to interact with people across cultural and language barriers, the risk of misunderstandings in communication also grows (Chang et al, 2020a).…”

Section: Discussionmentioning

confidence: 99%

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Fussell

Danescu-Niculescu-Mizil

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.

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“…For instance, the DepecheMood lexicon (Staiano and Guerini, 2014) was derived using statistical measures based on emotionally tagged text crawled from specific Web sites. Raji and de Melo (2020) revealed that unsupervised distributional semantics can outperform such supervised techniques. Leveau et al (2012) showed that word translations across languages are strongly correlated in emotion.…”

Section: Monolingual Emotion Lexicon Constructionmentioning

confidence: 99%

Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings

Ramachandran¹,

Melo

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually for over 350 languages, many of them resource-poor, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.

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What Sparks Joy: The AffectVec Emotion Database

Cited by 14 publications

References 20 publications

EmoTag1200: Understanding the Association between Emojis and Emotions

EmoTag1200: Understanding the Association between Emojis and Emotions

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings

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