Sender-intended functions of emojis in US messaging

Cramer, Henriette; Juan, Paloma de; Tetreault, Joel

doi:10.1145/2935334.2935370

Cited by 105 publications

(79 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While participants used GIFs in similar ways to emoji [14,23], we also found that participants appreciated how GIFs could convey more complex ideas and emotions, and how they signaled effort and engagement in the conversation. Participants tried to find the perfect GIFs to communicate not only these GIFs' evident meaning on the surface, but also personal common ground, such as "inside jokes" or unique experiences specific to communication partners, and communal common ground such as a reference to a movie that they watched together or belonging to the same community.…”

Section: The Benefits and Challenges Of Gifsmentioning

confidence: 61%

“…[5]. People use emoji in diverse ways: to provide additional emotional and situational information; change conversational tones; hide their true feelings; and maintain conversations and relationships with communication partners [14,23], which are also similar to the ways people use stickers [27]. Across cultures, people use emoji in similar ways [48], but the specific emoji they use differ [28].…”

Section: Emoticon and Emojimentioning

confidence: 99%

See 1 more Smart Citation

'The Perfect One'

Jiang

Fiesler

Brubaker

2018

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

Animated GIFs are increasingly popular in text-based communication. Finding the perfect GIF can make conversations funny, interesting, and engaging, but GIFs also introduce potentials for miscommunication. Through 24 in-depth qualitative interviews, this empirical, exploratory study examines the nuances of communication practices with animated GIFs to better understand why and how GIFs can send unintentional messages. We find participants leverage contexts like source material and interpersonal relationship to find the perfect GIFs for different communication scenarios, while these contexts are also the primary reason for miscommunication and some technical usability issues in GIFs. This paper concludes with a discussion of the important role that different types of context play in the use and interpretations of GIFs, and argues that nonverbal communication tools should account for complex contexts and common ground that communication media rely on.

show abstract

Section: The Benefits and Challenges Of Gifsmentioning

confidence: 61%

Section: Emoticon and Emojimentioning

confidence: 99%

'The Perfect One'

Jiang

Fiesler

Brubaker

2018

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Being encoded in Unicode, they have no language barriers and are diffused on the Internet rapidly [40]. The prevalence of emojis has attracted researchers from various research communities such as NLP, ubiquitous computing, human-computer interaction, multimedia, and Web mining [12,16,20,21,34,40,43]. Many efforts have been devoted to studying their usage across platforms [43], across genders [20], across languages [16], and across cultures [40].…”

Section: Related Workmentioning

confidence: 99%

“…Emojis are considered an emerging ubiquitous language used worldwide [16,40]; in our approach they serve both as a proxy of sentiment labels and as a bridge between languages. Their functionality of expressing emotions [21,34] motivates us to employ emojis as complementary labels for sentiments, while their ubiquity [16,40] makes it feasible to learn emoji-sentiment representations for almost every active language. Coupled with machine translation, the cross-language patterns of emoji usage can complement the pseudo parallel corpora and narrow the language gap, and the language-specific patterns of emoji usage help address the language discrepancy problem.…”

Section: Introductionmentioning

confidence: 99%

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Chen

Shen

et al. 2019

The World Wide Web Conference

View full text Add to dashboard Cite

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a considerable inequality in the quality of related information services received by users speaking different languages. To tackle this problem, crosslingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce.

show abstract

“…The notion of the similarity of two emoji is very broad. One can imagine a similarity measure based on the pixel similarity of emoji pictographs, yet this may not be useful since the pictorial representation of an emoji varies by mobile and computer platform [25,33,7]. Two similar looking pictographs may also correspond to emoji with radically different senses (e.g., twelve thirty and six o'clock , raised hand and raised back of hand , octopus , and squid , etc.)…”

Section: Introductionmentioning

confidence: 99%

A semantics-based measure of emoji similarity

Wijeratne

Balasuriya

Sheth

2017

Proceedings of the International Conference on Web Intelligence

View full text Add to dashboard Cite

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense de nitions, and with di erent training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http://emojinet.knoesis.org/.

show abstract

Sender-intended functions of emojis in US messaging

Cited by 105 publications

References 2 publications

'The Perfect One'

'The Perfect One'

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

A semantics-based measure of emoji similarity

Contact Info

Product

Resources

About