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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.

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.

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.