The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 405–409 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction Larisa Alexa, Alina Lorenț, Daniela Gîfu, Diana Trandabăț Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi Institute of Computer Science, Romanian Academy - Iasi Branch Cognos Business Consulting S.R.L., 32 Bd. Regina Maria, Bucharest, Romania {larisa.alexa04, alina.lorent}@gmail.com, {daniela.gifu, dtrandabat}@info.uaic.ro Abstract The “Multilingual Emoji Prediction” task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network). Keywords Virtual, emojis, human sentiment, understanding of emojis. 1. Introduction In the last few years, Social Media has evolved very fast, becoming at the moment a very important part of our daily life. There are several social networking platforms such as Twitter, Facebook, Instagram, WhatsApp, which were created in order to allow us to communicate with each other, to share our feelings or opinions related to different topics. Despite their differences or their purposes, each of these platforms shares one aspect: the use of emojis. From facial expressions to animals, objects or places, they are all used to communicate simple things or to enhance feelings and emotions. Due to the fact that their meaning is not always the same, processing emojis remains a challenge for the NLP researchers. From a language to another, for different cultures or depending on the user’s sentiments, emojis meaning can vary a lot. Understanding their meaning depending on the context of use has a huge relevance in multiple fields, like: human computer interaction, multimedia retrieval, etc. Twitter Emojis are pictures usually combined with text in order to emphasize the meaning of that text. Although these pictures are the same all over the world, they can be interpreted and used in different ways, depending on culture differences. Despite their widely usage in social media, their underlying semantics have received little attention from a Natural Language Processing standpoint. 2. Related work Over the past few years, there has been an increased public and enterprise interest in social media. Therefore, analyzing emojis has become an important aspect for NLP researchers, because their meaning has remained for the time unexplored. Go et al. [9] and Castellucci et al. [6] used in their papers distant supervision over emotion- labeled textual contents in order to train a sentiment classier and to build a polarity lexicon. Aoki et al. [1] described in his research a methodology to represent each emoticon as a vector of emotions, while Jiang [10] proposed a sentiment and emotion classier based on semantic spaces of emojis in the Chinese Website Sina Weibo. In his research, Cappallo et al. [5] proposed a multimodal approach for generating emoji labels for images (Image2Emoji). Boia et al. (2013) [4] analyzed sentiment lexicons generated by considering emoticons, showing that in many cases …

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