Incorporating Emoji Descriptions Improves Tweet Classification

Singh, Abhishek; Blanco, Eduardo; Jin, Wei

doi:10.18653/v1/n19-1214

Cited by 52 publications

(36 citation statements)

References 16 publications

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“…Wang et al (2016) designed a hybrid sentimental entity recognition model (HSERM), which classifies emoji into four different emotional categories, and then categorizes the emotional data based on the model. Some research has focused on the ironic features of emoji and developed an irony detection model for emoji in order to improve the accuracy of sentiment analysis of tweets (Reyes et al, 2013; Prasad et al, 2017; Singh et al, 2019).…”

Section: Research Fields Regarding Emojimentioning

confidence: 99%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.

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Section: Research Fields Regarding Emojimentioning

confidence: 99%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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“…3) Emoji. Inspire by Singh et al [30], we convert emoji to natural language description such as "face with tears of joy" to ensure that our models understand user's emotion.…”

Section: Data Preprocessingmentioning

confidence: 99%

Leverage Social Media for Personalized Stress Detection

Wang

Zhang

Cao

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Timely detection of stress is desirable to address the increasingly serious stress problem. Thanks to the rich linguistic expressions and complete historical records on social media, achieving personalized stress detection through social media is feasible and prominent. We construct a three-leveled framework, aiming at personalized stress detection based on social media. The three-leveled framework learns the personalized stress representations following an increasingly detailed processing, i.e., from the generic mass level, group level, to the final individual level. The first mass-level focuses on mining the generic stress representations from people's linguistic and visual posts with a two-layer attention mechanism. The second group-level adopts the graph neural network to learn the groupwise characteristics of the group where an individual belongs to. The third individual-level analyzes and incorporates individual's personality traits into stress detection. The performance study on the 2,059 microblog users shows that our proposed method can achieve over 90% in detection accuracy. Furthermore, the extended experiment on a harder personalized sub-dataset demonstrates that our method works better in distinguishing personalized expressions with different latent meanings. CCS CONCEPTS • Applied computing → Psychology; • Human-centered computing → Social media; • Information systems → Sentiment analysis.

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“…Hayati et al (2019) designed experiments to show the interconnections between emoji usage and ironic expressions. Singh et al (2019) also evaluated the influence of emojis on irony detection and sentiment analysis tasks, but they replaced emojis with descriptive text in this process. Based on previous knowledge about emojis and the emoji prediction task, we also use sentiment analysis, emotion recognition, and formality classification tasks to validate the quality of our annotations in this paper.…”

Section: Related Workmentioning

confidence: 99%

Multi-resolution Annotations for Emoji Prediction

Ma¹,

Liu²,

Wang³

et al. 2020

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

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Emojis are able to express various linguistic components, including emotions, sentiments, events, etc. Predicting the proper emojis associated with text provides a way to summarize the text accurately, and it has been proven to be a good auxiliary task to many Natural Language Understanding (NLU) tasks. Labels in existing emoji prediction datasets are all passage-based and are usually under the multi-class classification setting. However, in many cases, one single emoji cannot fully cover the theme of a piece of text. It is thus useful to infer the part of text related to each emoji. The lack of multi-label and aspectlevel emoji prediction datasets is one of the bottlenecks for this task. This paper annotates an emoji prediction dataset with passage-level multi-class/multi-label, and aspect-level multiclass annotations. We also present a novel annotation method with which we generate the aspect-level annotations. The annotations are generated heuristically, taking advantage of the self-attention mechanism in Transformer networks. We validate the annotations both automatically and manually to ensure their quality. We also benchmark the dataset with a pretrained BERT model.

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Incorporating Emoji Descriptions Improves Tweet Classification

Cited by 52 publications

References 16 publications

A Systematic Review of Emoji: Current Research and Future Perspectives

A Systematic Review of Emoji: Current Research and Future Perspectives

Leverage Social Media for Personalized Stress Detection

Multi-resolution Annotations for Emoji Prediction

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