Tech, Marketing and Strategy
All you ever wanted to know about Emojis but were afraid to ask.
You may have thought that emojis are just for fun, but now scientists are getting beyond the laughing smiling face of emojis to discover their role in emotions, expression and communication.
More than 90 per cent of people use emojis and emoticons. Studies have found that emojis can help with cross-cultural communication, communication with children and those with special needs.
Emojis also provide insights into user personalities. Psychologists are researching how emojis and emoticons function as tools for expression - and finding that they are a form of digital body language.
In face-to-face communication, verbal and nonverbal cues (like facial movements, voice pitch, and shaking fists for example) are essential to understanding the meaning of what we are communicating.
"We mostly use emojis like gestures, as a way of enhancing emotional expressions," says cyberpsychologist Linda Kaye, at Edge Hill University talking to Gizmodo. "There are a lot of idiosyncrasies in how we gesture, and emojis are similar to that, especially because of the discrepancies as to how and why we use them."
Research also shows that propensity to use emoticons may actually be more about personality type than age. A 2014 survey of 1,000 people in the US showed only 54 per cent of emoticon users were 18 to 34.
"If you look at personality traits, like agreeableness, how amenable you are to other people, it seems to be related to whether you use emojis or not," Kaye says.
Psychologists also want to use online data to understand how communicating via emojis and emoticons can provide insights into social inclusion. Depending on how we use emojis, these simple displays of virtual emotion can impact how we perceive each other.
"People are making judgments about us based on how we use emojis, and they're not necessarily accurate," Kaye says. "What we need to be aware of is that those judgments might differ depending on where or with whom you're using those emojis, such as in the workplace or between family members.
Abstract: NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-the-art performance on 8 benchmark datasets within sentiment, emotion and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.
Pub.: 01 Aug '17, Pinned: 14 Aug '17
Abstract: There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.
Pub.: 08 Dec '15, Pinned: 05 Jun '17
Abstract: Authors: Jennifer Fane ; Colin MacDougall ; Jessie Jovanovic ; Gerry Redmond ; Lisa Gibbs Article URL: http://www.tandfonline.com/doi/full/10.1080/03004430.2016.1219730?ai=z4&mi=3fqos0&af=R Citation: Early Child Development and Care Publication Date: 2016-08-17T01:56:46Z Journal: Early Child Development and Care
Pub.: 17 Aug '16, Pinned: 05 Jun '17
Abstract: Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation.
Pub.: 27 Sep '16, Pinned: 05 Jun '17
Abstract: Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or sense of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at http://emojinet.knoesis.org.
Pub.: 24 Oct '16, Pinned: 05 Jun '17
Abstract: We report on an exploratory analysis of Emoji Dick, a project that leverages crowdsourcing to translate Melville's Moby Dick into emoji. This distinctive use of emoji removes textual context, and leads to a varying translation quality. In this paper, we use statistical word alignment and part-of-speech tagging to explore how people use emoji. Despite these simple methods, we observed differences in token and part-of-speech distributions. Experiments also suggest that semantics are preserved in the translation, and repetition is more common in emoji.
Pub.: 07 Nov '16, Pinned: 05 Jun '17
Abstract: Building Part-of-Speech (POS) taggers for code-mixed Indian languages is a particularly challenging problem in computational linguistics due to a dearth of accurately annotated training corpora. ICON, as part of its NLP tools contest has organized this challenge as a shared task for the second consecutive year to improve the state-of-the-art. This paper describes the POS tagger built at Surukam to predict the coarse-grained and fine-grained POS tags for three language pairs - Bengali-English, Telugu-English and Hindi-English, with the text spanning three popular social media platforms - Facebook, WhatsApp and Twitter. We employed Conditional Random Fields as the sequence tagging algorithm and used a library called sklearn-crfsuite - a thin wrapper around CRFsuite for training our model. Among the features we used include - character n-grams, language information and patterns for emoji, number, punctuation and web-address. Our submissions in the constrained environment,i.e., without making any use of monolingual POS taggers or the like, obtained an overall average F1-score of 76.45%, which is comparable to the 2015 winning score of 76.79%.
Pub.: 31 Dec '16, Pinned: 05 Jun '17
Abstract: Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smooth the communications between users, making dialogue systems more anthropomorphic and vivid. In this paper, we focus on automatically recommending appropriate emojis given the contextual information in multi-turn dialogue systems, where the challenges locate in understanding the whole conversations. More specifically, we propose the hierarchical long short-term memory model (H-LSTM) to construct dialogue representations, followed by a softmax classifier for emoji classification. We evaluate our models on the task of emoji classification in a real-world dataset, with some further explorations on parameter sensitivity and case study. Experimental results demonstrate that our method achieves the best performances on all evaluation metrics. It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.
Pub.: 14 Dec '16, Pinned: 05 Jun '17
Abstract: Emojis, as a new way of conveying nonverbal cues, are widely adopted in computer-mediated communications. In this paper, first from a message sender perspective, we focus on people's motives in using four types of emojis -- positive, neutral, negative, and non-facial. We compare the willingness levels of using these emoji types for seven typical intentions that people usually apply nonverbal cues for in communication. The results of extensive statistical hypothesis tests not only report the popularities of the intentions, but also uncover the subtle differences between emoji types in terms of intended uses. Second, from a perspective of message recipients, we further study the sentiment effects of emojis, as well as their duplications, on verbal messages. Different from previous studies in emoji sentiment, we study the sentiments of emojis and their contexts as a whole. The experiment results indicate that the powers of conveying sentiment are different between four emoji types, and the sentiment effects of emojis vary in the contexts of different valences.
Pub.: 08 Mar '17, Pinned: 05 Jun '17
Abstract: Notwithstanding several authors have recognised the conceptual key of "politics" as an important component in any Require-ments Engineering (RE) process, practitioners still lack a prag-matic answer on how to deal with the political dimension: such an ability has become a mostly desirable but totally undetailed part of what we usually and vaguely refer to as "professional experience". Nor were practitioners given any suitable tool or method to easily detect, represent, control and if possible leverage politics. Authors argue that this issue could be successfully ad-dressed and resolved if, when we map organisations against the system to be developed, we include power and politics in their "too human" and even emotional dimension. A simple way to do so is to use emoji pictograms: most of them are part of a universal language, which requirements engineers could easily adopt and exploit to assess and produce models that include an extra layer of "political" information, without the need to actually introduce any new notation. A few examples of emoji-aware UML and organisational charts are hereby proposed, more as a platform to support communication and share reflections on how to deal with politics than as an actual technology to be adopted.
Pub.: 17 Mar '17, Pinned: 05 Jun '17
Abstract: The use of emoticons and emoji is increasingly popular across a variety of new platforms of online communication. They have also become popular as stimulus materials in scientific research. However, the assumption that emoji/emoticon users' interpretations always correspond to the developers'/researchers' intended meanings might be misleading. This article presents subjective norms of emoji and emoticons provided by everyday users. The Lisbon Emoji and Emoticon Database (LEED) comprises 238 stimuli: 85 emoticons and 153 emoji (collected from iOS, Android, Facebook, and Emojipedia). The sample included 505 Portuguese participants recruited online. Each participant evaluated a random subset of 20 stimuli for seven dimensions: aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. Participants were additionally asked to attribute a meaning to each stimulus. The norms obtained include quantitative descriptive results (means, standard deviations, and confidence intervals) and a meaning analysis for each stimulus. We also examined the correlations between the dimensions and tested for differences between emoticons and emoji, as well as between the two major operating systems-Android and iOS. The LEED constitutes a readily available normative database (available at www.osf.io/nua4x ) with potential applications to different research domains.
Pub.: 02 Apr '17, Pinned: 05 Jun '17
Abstract: Emojis have gained incredible popularity in recent years and become a new ubiquitous language for Computer-Mediated Communication (CMC) by worldwide users. Various research efforts have been made to understand the behaviors of using emojis. Gender-specific study is always meaningful for HCI community, however, so far we know very little about whether and how much males and females vary in emoji usage. To bridge such a knowledge gap, this paper makes the first effort to explore the emoji usage through a gender lens. Our analysis is based on the largest data set to date, which covers 134,419 users from 183 countries, along with their over 401 million messages collected in three months. We conduct a multi-dimensional statistical analysis from various aspects of emoji usage, including the frequency, preferences, input patterns, public/private CMC-scenario patterns, temporal patterns, and sentiment patterns. The results demonstrate that emoji usage can significantly vary between males and females. Accordingly, we propose some implications that can raise useful insights to HCI community.
Pub.: 16 May '17, Pinned: 05 Jun '17