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The Semiotics of Emoji
Shortlisted for the BAAL Book Prize 2017 Emoji have gone from being virtually unknown to being a central topic in internet communication. What is behind the rise and rise of these winky faces, clinking glasses and smiling poos? Given the sheer variety of verbal communication on the internet and English's still-controversial role as lingua mundi for the web, these icons have emerged as a compensatory universal language. The Semiotics of Emoji looks at what is officially the world's fastest-growing form of communication. Emoji, the colourful symbols and glyphs that represent everything from frowning disapproval to red-faced shame, are fast becoming embedded into digital communication. Controlled by a centralized body and regulated across the web, emoji seems to be a language: but is it? The rapid adoption of emoji in such a short span of time makes it a rich study in exploring the functions of language. Professor Marcel Danesi, an internationally-known expert in semiotics, branding and communication, answers the pertinent questions. Are emoji making us dumber? Can they ultimately replace language? Will people grow up emoji literate as well as digitally native? Can there be such a thing as a Universal Visual Language? Read this book for the answers.
eBook
Emoticons, kaomoji, and emoji : the transformation of communication in the digital age
\"This collection offers a comprehensive treatment of emoticons, kaomoji, and emoji, examining these digital pictograms and ideograms from a range of perspectives to comprehend their increasing role in the transformation of communication in the digital age. Featuring a detailed introduction and eleven contributions from an interdisciplinary group of scholars, the volume begins by outlining the history and development of the field, situating emoticons, kaomoji, and emoji - expressing a variety of moods and emotional states, facial expressions, as well as all kinds of everyday objects- as both a topic of global relevance but also within multimodal, semiotic, picture theoretical, cultural and linguistic research. The book shows how the interplay of these systems with text can alter and shape the meaning and content of messaging and examines how this manifests itself through different lenses, including the communicative, socio-political, aesthetic, and cross-cultural. Making the case for further study on emoticons, kaomoji, and emoji and their impact on digital communication, this book is key reading for students and scholars in sociolinguistics, media studies, Japanese studies, and language and communication\"-- Provided by publisher.
Book
Service with Emoticons
Virtually no research has examined the role of emoticons in commercial relationships, and research outside the marketing domain reports mixed findings. This article aims to resolve these mixed findings by considering that emoticon senders are often simultaneously evaluated on two fundamental dimensions, warmth and competence, and the accessibility of one dimension over the other is critically contingent on salient relationship norms (communal vs. exchange norms) in customers’ minds due to individual and situational factors. Through laboratory and field experiments, the current research shows that customers perceive service employees who use emoticons as higher in warmth but lower in competence compared to those who do not (study 1). We further demonstrate that when a service employee uses emoticons, communal-oriented (exchange-oriented) customers are more likely to infer higher warmth (lower competence) and thus to be more (less) satisfied with the service (study 2). We also examine two practically important service situations that can make a certain type of relationship norm more salient: unsatisfactory services (study 3) and employees’ extra-role services (study 4). We speculate on possible mechanisms underlying these effects and discuss theoretical and practical implications along with opportunities for future research.
Journal Article
Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review
Sentiment analysis is an emerging trend nowadays to understand people’s sentiments in multiple situations in their quotidian life. Social media data would be utilized for the entire process ie the analysis and classification processes and it consists of text data and emoticons, emojis, etc. Many experiments were conducted in the antecedent studies utilizing Binary and Ternary Classification whereas Multi-class Classification gives more precise and precise Classification. In Multi-class Classification, the data would be divided into multiple sub-classes predicated on the polarities. Machine Learning and Deep Learning Techniques would be utilized for the classification process. Utilizing Social media, sentiment levels can be monitored or analysed. This paper shows a review of the sentiment analysis on Social media data for apprehensiveness or dejection detection utilizing various artificial intelligence techniques. In the survey, it was optically canvassed that social media data which consists of texts,emoticons and emojis were utilized for the sentiment identification utilizing various artificial intelligence techniques. Multi Class Classification with Deep Learning Algorithm shows higher precision value during the sentiment analysis.
Journal Article
Worth a thousand interpersonal words: Emoji as affective signals for relationship-oriented digital communication
Computer-mediated communication (CMC) is pervasive in our lives, influencing social interaction including human courtship. To connect with potential partners via CMC, modern relationship-seekers must master faster and shorter methods of communicating self-disclosure and affect. Although CMC can lack crucial sensory information in this context, emojis may provide useful aid. Across two studies, we assessed attitudes toward and frequency of emoji use, and whether signaling affect via emoji use relates to more romantic and sexual opportunities. Our findings suggest that emoji use with potential partners is associated with maintaining connection beyond the first date, and more romantic and sexual interactions over the previous year. This research provides evidence that emojis convey important affective information to potential partners, and are potentially associated with more successful intimate connection. Implications for multiple theoretical models and methodologies are discussed.
Journal Article
Color Affects Recognition of Emoticon Expressions
In computer-mediated communication, emoticons are conventionally rendered in yellow. Previous studies demonstrated that colors evoke certain affective meanings, and face color modulates perceived emotion. We investigated whether color variation affects the recognition of emoticon expressions. Japanese participants were presented with emoticons depicting four basic emotions (Happy, Sad, Angry, Surprised) and a Neutral expression, each rendered in eight colors. Four conditions (E1–E4) were employed in the lab-based experiment; E5, with an additional participant sample, was an online replication of the critical E4. In E1, colored emoticons were categorized in a 5AFC task. In E2–E5, stimulus affective meaning was assessed using visual scales with anchors corresponding to each emotion. The conditions varied in stimulus arrays: E2: light gray emoticons; E3: colored circles; E4 and E5: colored emoticons. The affective meaning of Angry and Sad emoticons was found to be stronger when conferred in warm and cool colors, respectively, the pattern highly consistent between E4 and E5. The affective meaning of colored emoticons is regressed to that of achromatic expression counterparts and decontextualized color. The findings provide evidence that affective congruency of the emoticon expression and the color it is rendered in facilitates recognition of the depicted emotion, augmenting the conveyed emotional message.
Journal Article
Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title
Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.
Journal Article
Sentiment Classification Method Based on Blending of Emoticons and Short Texts
With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.
Journal Article