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Analyzing and Predicting Emoji Usages in Social Media Track: Cognitive Computing WWW 2018, April 23-27, 2018, Lyon, France Analyzing and Predicting Emoji Usages in Social Media Peijun Zhao Jia Jia∗ Yongsheng An [email protected] [email protected] [email protected] Department of Computer Science and Department of Computer Science and Academy of Arts & Design, Tsinghua Technology, Tsinghua University Technology, Tsinghua University University Key Laboratory of Pervasive Key Laboratory of Pervasive Computing, Ministry of Education Computing, Ministry of Education Tsinghua National Laboratory for Tsinghua National Laboratory for Information Science and Technology Information Science and Technology (TNList) (TNList) Jie Liang Lexing Xie Jiebo Luo [email protected] [email protected] [email protected] Academy of Arts & Design, Tsinghua Department of Computer Science, Department of Computer Science, University Australian National University University of Rochester ABSTRACT 1 INTRODUCTION Emojis can be regarded as a language for graphical expression Emojis have been widely used in social networks to express more of emotions, and have been widely used in social media. They delicate feelings beyond textual information and make computer- can express more delicate feelings beyond textual information and mediated communication more effective. Vulture’s Lindsey Weber, improve the effectiveness of computer-mediated communication. who co-curated the “Emoji” art show1, says that she use emojis in Recent advances in machine learning make it possible to automatic personal emails all the time, because she feel like she is softening the compose text messages with emojis. However, the usages of emojis email. According to the study in Instagram[4], emojis are present can be complicated and subtle so that analyzing and predicting in up to 40% of online messages in many countries. For example, emojis is a challenging problem. In this paper, we first construct a “Face with Tears of Joy”, an emoji that means the person has an benchmark dataset of emojis with tweets and systematically inves- extremely good mood, is regarded as the 2015 word of the year by tigate emoji usages in terms of tweet content, tweet structure and The Oxford Dictionary. user demographics. Inspired by the investigation results, we further Since most people have experiences in using emojis for posting propose a multitask multimodality gated recurrent unit (mmGRU) tweets, the problem of automatically generating emojis becomes model to predict the categories and positions of emojis. The model interesting and useful for online users. Existing works usually study leverages not only multimodality information such as text, image emoji usages based on textual information. [8] uses Affective Tra- and user demographics, but also the strong correlations between jectory Model for emoji category recommendation based on textual emoji categories and their positions. Our experimental results show information. [16] analyzes the emotion of the textual information that the proposed method can significantly improve the accuracy published by users and proposes an emoji recommendation method for predicting emojis for tweets (+9.0% in F1-value for category and based on the emotive statements of users and their past selections +4.6% in F1-value for position). Based on the experimental results, of emojis. However, whether there are regularities or norms in we further conduct a series of case studies to unveil how emojis the choices of emojis and what information is related to them are are used in social media. still open problems, which make the problem of generating emojis challenging. KEYWORDS The usages of emojis depend on many complex factors. Since Emoji; GRU; Multimodality; Multitask emojis are an integral part of tweets and play an important role in expressing emotion, the choices of emojis are mainly affected by the ACM Reference Format: following factors: 1) Tweet content. Not only textual information, Peijun Zhao, Jia Jia, Yongsheng An, Jie Liang, Lexing Xie, and Jiebo Luo. but also visual information can enrich the expression of emotion for 2018. Analyzing and Predicting Emoji Usages in Social Media. In WWW ’18 tweets. How can the multimodality information affect the choices of Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3184558. emojis? 2) Tweet structure. Different emojis may appear at different 3186344 positions in tweets. Whether there are correlations between the categories and positions of emojis has not yet been confirmed. 3) This paper is published under the Creative Commons Attribution 4.0 International User demographics. Different users may have different habits on (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their using emojis. How can user demographics such as geographica personal and corporate Web sites with the appropriate attribution. WWW ’18 Companion, April 23–27, 2018, Lyon, France region information influence assigning emojis? © 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License. ACM ISBN 978-1-4503-5640-4/18/04. ?Corresponding author https://doi.org/10.1145/3184558.3186344 1http://www.emojishow.com/ 327 Track: Cognitive Computing WWW 2018, April 23-27, 2018, Lyon, France Figure 1: Overview of the Proposed Emoji Prediction Method. Far-left: Example tweets from Twitter. Mid-left: Textual, visual and user demographic features. Mid-right: The mmGRU model. Far-right: Predictions results. In this paper, we first construct a benchmark dataset of emojis notations and formulate the learning tasks in section 3 and discuss from Twitter. It contains 164,880 tweets of the 35 most frequently data observation in section 4. Next, we describe the proposed emoji used emojis. Using the dataset, we systematically study emoji us- prediction method in section 5. Experiments are given in section 6 ages with tweet content, tweet structure and user demographics and and section 7 concludes this paper. obtain a series of data-driven observations. Inspired by the obser- vations, we propose a multitask multimodality gated recurrent unit 2 RELATED WORK (mmGRU) model (Figure 1) to quantitatively study the correlations Social emotion analysis. People often use emojis to express emo- between emojis and the above three aspects. The multimodality tions. Research on emotion analysis or prediction has been dis- function is used to incorporate multimodality information such cussed in many works. [1, 20] describe sentiment analysis methods as text, image, and user demographics, while the multitask frame- using textual and visual contents. [21] proposes a new neighbor- work contributes to leveraging the strong correlations between hood classier based on the similarity of two instances described emoji categories and their positions for improving the performance by the fusion of textual and visual features. Recent years, studies of both tasks. We apply the proposed model to predict the emoji have found that user demographics can also help for sentiment categories and positions of online tweets in our dataset. Our exper- analysis for online users. [18] studies the influence of user demo- imental results show that our model can significantly improve the graphics on image sentiment analysis. [19] combines the textual, prediction performance of emoji category by +9.0% in F1-value and visual and the social information of users and propose a user-level emoji position by +4.6% in F1-value, on the average, compared with emotion prediction method. The above works reveal that adopting several alternative baselines. Based on the experimental results, we multimodality information and user demographics can improve further conduct a series of case studies to reveal how emojis are personalized emotion prediction. used in social media. Emoji Usages and Recommendation. Traditional works usu- Our main contributions are as follows: ally study emoji usages through two aspects: treat emojis as sen- • We construct a large-scale benchmark dataset for emoji timental labels, and recommend emojis with textual information. prediction in terms of emoji categories and positions. Each For sentiment analysis, [22] divides emojis into four kinds of emo- tweet in our dataset contains both textual, visual and the tions and then uses them to study emotion changes of online users. user demographic information. We release the dataset and [15] studies the multilingual sentiment analysis using emojis and 2 related information upon the publication of this work . keywords. [11] labels the sentiment scores of emojis. [6] builds an • We investigate the correlation between emoji usages and emoji space model for sentiment analysis. [10] studies the senti- three key aspects. We then propose an mmGRU model to ment and semantic misconstrue of emojis on different platforms. predict the categories and positions of emojis. Our experi- [5] studies the intentions and sentiment effects of using emojis mental results validate the rationality and effectiveness of during communications. While for emoji recommendation, [8] uses the proposed mmGRU model. Affective Trajectory Model for emoji category recommendation • We unveil several interesting findings based on our data, based on textual information. [16] analyzes the emotion of the tex- method and experiments, including: 1) users in different ge- tual information and proposes an emoji recommendation method ographic regions have different diversity in emoji selection;
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