EmoTag – Towards an Emotion-Based Analysis of Emojis

Shoeb, Abu Awal Md; Raji, Shahab; Melo, Gerard de

doi:10.26615/978-954-452-056-4_126

Cited by 10 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Assessing Emoji Use in Modern Text Processing Tools

Shoeb¹,

Melo²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

Self Cite

View full text Add to dashboard Cite

Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. This also leads to an increased need for systems and tools to operate on text containing emojis. In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. In particular, we consider tokenization, part-of-speech tagging, dependency parsing, as well as sentiment analysis. Our findings show that many systems still have notable shortcomings when operating on text containing emojis.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing Emoji Use in Modern Text Processing Tools

Shoeb¹,

Melo²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…Rakhmetullina et al (2018) categorized a set of 15 emojis into 4 different emotion classes, while Li et al (2019) used a lexicon-based heuristic to compare connections between emojis and emotions in social media data. Several studies have explored the linguistic connection between words and emojis (Cappallo et al, 2019;Barbieri et al, 2017;Na'aman et al, 2017;Shoeb et al, 2019). However, previous work has not assessed to what extent humans associate particular emotions with different emojis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EmoTag1200: Understanding the Association between Emojis and Emotions

Shoeb¹,

Melo²

2020

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…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%

Domain-Specific Sentiment Lexicons Induced from Labeled Documents

Islam¹,

Dong

Melo

2020

Proceedings of the 28th International Conference on Computational Linguistics

Self Cite

View full text Add to dashboard Cite

Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies. Although supervised learning algorithms have advanced considerably in recent years, in many settings it remains more practical to apply an unsupervised technique. The latter are oftentimes based on sentiment lexicons. However, existing sentiment lexicons reflect an abstract notion of polarity and do not do justice to the substantial differences of word polarities between different domains. In this work, we draw on a collection of domainspecific data to induce a set of 24 domain-specific sentiment lexicons. We rely on initial linear models to induce initial word intensity scores, and then train new deep models based on word vector representations to overcome the scarcity of the original seed data. Our analysis shows substantial differences between domains, which make domain-specific sentiment lexicons a promising form of lexical resource in downstream tasks, and the predicted lexicons indeed perform effectively on tasks such as review classification and cross-lingual word sentiment prediction.

show abstract

EmoTag – Towards an Emotion-Based Analysis of Emojis

Cited by 10 publications

References 20 publications

Assessing Emoji Use in Modern Text Processing Tools

Assessing Emoji Use in Modern Text Processing Tools

EmoTag1200: Understanding the Association between Emojis and Emotions

Domain-Specific Sentiment Lexicons Induced from Labeled Documents

Contact Info

Product

Resources

About