Emoji Prediction Research Articles

B-TTDb: A Database of Turkish Tweets for Predicting the Top One Hundred Emojis

Emoji prediction is an important research task that focuses on finding the most appropriate emoji(s) quickly and effortlessly for a specific text. Now that Turkish is on the list of the top 20 most spoken languages in the world and there are a considerable number of Turkish-speaking social media users, studying emoji prediction in Turkish holds significant value. In this study, a Turkish tweets database, named Bitirim's Turkish Tweets Database (B-TTDb), was constructed for academic and industrial studies based on the prediction of the top 100 emojis. B-TTDb consists of four datasets. The first dataset includes raw tweets, the second dataset is the organized version of the first dataset, the third dataset is the pre-processed version of the second dataset, and the last one is the organized version of the third dataset. The last one is the final version and it is named Bitirim's Dataset (B-D). It includes a total of 158,201 unique tweets belonging to the top 100 emoji classes. For database validation, experiments were conducted on B-D with popular machine learning algorithms for the top 10, 20, 50, and 100 emojis. This study could be considered as the first study that contributes to the literature by the first validated large database of Turkish tweets that includes such a large number of emojis. In addition, B-TTDb could be a basis as well as motivation for various further studies.

Comparative analysis of Deep Learning and Machine Learning algorithms for emoji prediction from Arabic text

Comparative analysis of Deep Learning and Machine Learning algorithms for emoji prediction from Arabic text

Green Edge Intelligence Scheme for Mobile Keyboard Emoji Prediction

Emoji prediction has been widely adopted in most mobile keyboards to improve the quality of user experience. Considering the resource constraints of smartphones, it is promising to deploy well-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, a key issue in such a scenario lies in how the smartphone should select a subset of models to achieve high-accuracy and real-time emoji prediction with energy efficiency ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a.k.a.</i> the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model selection</i> problem). Moreover, part of the system dynamics such as the prediction accuracy and the inference latency of each model are usually unknown <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> in practice, further complicating the problem. In this paper, with an effective integration of history-aware online learning and online control, we propose the first green edge intelligence scheme to solve the model selection problem for mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sub-linear round-averaged regret bound and energy efficiency with a high prediction accuracy and a low latency.

Federated Learning-Based Architecture for Personalized Next Emoji Prediction for Social Media Comments

Federated Learning-Based Architecture for Personalized Next Emoji Prediction for Social Media Comments

TGCN-Bert Emoji Prediction in Information Systems Using TCN and GCN Fusing Features Based on BERT

In recent studies, graph convolutional neural networks (GCNs) have been used to solve different natural language processing (NLP) tasks. However, few researches apply graph convolutional networks to short text classification. Emoji prediction, as a complex sentiment analysis task, has received even less attention. In this work, the authors propose TGCN-Bert which combines pre-trained BERT temporal convolutional networks (TCNs) and graph convolutional networks for short text classification and emoji prediction. They initialize the nodes with the help of BERT and define the edges in text graph based on the term frequency-inverse document frequency (TF-IDF) and positive point-wise mutual information (PPMI). They employ the model for emoji prediction task, and a metric based on emoji clustering is developed to better measure the validity of emoji prediction results. To validate the performance of TGCN-Bert, they compare it with other GCN variants on short text classification datasets and emoji prediction datasets; experiments show that TGCN-Bert achieves better performance.

Unity in Diversity: Multilabel Emoji Identification in Tweets

Emojis or emoticons are not just a modern trend but have become an essential part of our day-to-day interactions. Predicting a suitable emoji for a given tweet is a challenging task because a wrong emoji prediction for a tweet can change the meaning of the message or can amplify the emotion of the message. This task is particularly challenging since it requires selecting an appropriate emoji from a huge list of prospective emojis that may or may not be equivalent to one another. Humans use multiple emojis to convey their emotions, thereby making the task a multilabel classification problem. In this article, we propose a multilabel emoji prediction system that predicts the appropriate emoji for a given tweet by using different state-of-the-art baselines. Due to the unavailability of a multi-emoji dataset, we create a large-scale multilabel emoji dataset named Mu-Emoji that comprises of more than 0.6 million tweets having varied emojis belonging to both positive and negative sentiments. For our proposed task, we employ graph attention network along with bidirectional encoder representations from transformer encoder for the accurate prediction of emojis. Qualitative and quantitative analyses show that our multilabel emoji prediction baselines perform well compared with the single-emoji prediction baselines for our proposed Mu-Emoji dataset. Our proposed framework also outperforms all the baselines for both single and multilabel emoji prediction tasks.

Emoji Prediction Using Bi-Directional LSTM

Messengers and social media dominate today’s internet usage across the globe. For the large population, a typical day starts with messages flooding on mobiles, from simple good morning wishes, business meeting invites, reminders, and schedules for the day and the list is endless. A striking feature of today’s digital communication is the variety of emojis used, without which text communication almost look incomplete. Emojis are graphic symbols/logograms used with text communication to enhance the effectiveness of emotions and set an undertone that makes texting a more fun experience for the users. Emojis are the visual language of the new generation. They give consumers a means to communicate their feelings while reducing the quantity of text that needs to be typed by the sender. Every social media and messenger platform like Facebook, Instagram, Twitter, WhatsApp, and many more have its own emoji set. To lure more and more users, many new emojis are added day by day. Predicting and suggesting emojis based on the text, emotion and user patterns to the user is an important feature of today’s messengers and social media applications. If you start typing a message, relevant emojis will be displayed from which users can choose an emoji, further enhancing the user texting experience. This process is done using natural language processing and machine learning techniques. In this paper, we study emoji prediction techniques and propose an emoji prediction model using bi-directional LSTMs. We compare emoji prediction NLP techniques, including RNN, LSTM, LSTM networks, and Bi-LSTM. Based on our implementation, we suggest that the bi-directional LSTM model is the most effective technique. Our model outperforms many baseline approaches with an accuracy of 94% when tested on a CodaLab Twitter data set with 60000 rows and two columns. Our study shows the effectiveness and efficiency of bi-directional LSTMs for text-based systems for communication.

EmoRile: a personalised emoji prediction scheme based on user profiling

EmoRile: a personalised emoji prediction scheme based on user profiling

EmoRile: A Personalized Emoji Prediction Scheme Based on User Profiling

EmoRile: A Personalized Emoji Prediction Scheme Based on User Profiling

A self-attention hybrid emoji prediction model for code-mixed language: (Hinglish)

A self-attention hybrid emoji prediction model for code-mixed language: (Hinglish)

One emoji, many meanings: A corpus for the prediction and disambiguation of emoji sense

In this work, we uncover a hidden linguistic property of emoji, namely that they are polysemous and can be used to form a semantic network of emoji meanings. Our key contributions to this direction of study are as follows: (1) We have developed a new corpus to help in the task of emoji sense prediction. This corpus contains tweets with single emojis, where each emoji has been labelled with an appropriate sense identifier from WordNet. (2) Experiments, which demonstrate that it is possible to predict the sense of an emoji using our corpus to a reasonable level of accuracy. We are able to report an average path-similarity score of 0.4146 for our best emoji sense prediction algorithm. (3) We further show that emoji sense is a useful feature in the emoji prediction task, where we report an accuracy of 58.8816 and macro-F1 score of 46.6640, beating reasonable baselines in this task. Our work demonstrates that importance of considering the meaning behind emoji, rather than ignoring them, or simply treating them as extra wordforms.

MultiEmo: Multi-task framework for emoji prediction

Emojis are becoming a new visual and linguistic tool that allows users to express their feelings and communicate with each other on social media. Driven by the importance of emoji interpretation, an emoji prediction task, which aims to predict the most likely emojis within a text, has gained significant attention. Thus, we propose a new deep neural network model for this task, MultiEmo (multi-task framework for emoji prediction), to predict the most relevant emoji by considering the emotion detection task. Our experiment shows that MultiEmo is superior to existing models using the Twitter dataset, implying that the models can learn richer representations from semantically related tasks. We systematically confirm that each emoji is associated with a particular emotion in a similar context. We also introduce new evaluation metrics to measure the comprehensive performance of the models.

Emojis as Contextual Indicants in Location-Based Social Media Posts

The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants.

Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication Data

Sentiment and emotion detection from textual communication records of developers have various application scenarios in software engineering (SE). However, commonly used off-the-shelf sentiment/emotion detection tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to create labeled SE-related datasets manually and customize SE-specific methods. However, the scarce labeled data can cover only very limited lexicon and expressions. In this article, we employ emojis as an instrument to address this problem. Different from manual labels that are provided by annotators, emojis are self-reported labels provided by the authors themselves to intentionally convey affective states and thus are suitable indications of sentiment and emotion in texts. Since emojis have been widely adopted in online communication, a large amount of emoji-labeled texts can be easily accessed to help tackle the scarcity of the manually labeled data. Specifically, we leverage Tweets and GitHub posts containing emojis to learn representations of SE-related texts through emoji prediction. By predicting emojis containing in each text, texts that tend to surround the same emoji are represented with similar vectors, which transfers the sentiment knowledge contained in emoji usage to the representations of texts. Then we leverage the sentiment-aware representations as well as manually labeled data to learn the final sentiment/emotion classifier via transfer learning. Compared to existing approaches, our approach can achieve significant improvement on representative benchmark datasets, with an average increase of 0.036 and 0.049 in macro-F1 in sentiment and emotion detection, respectively. Further investigations reveal that the large-scale Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource but try to transform knowledge from the open domain through ubiquitous signals such as emojis. Finally, we present the open challenges of sentiment and emotion detection in SE through a qualitative analysis of texts misclassified by our approach.

Seq2Emoji: A hybrid sequence generation model for short text emoji prediction

As a new form of visual language, emojis are widely used in social media for their vivid image and rich meaning. Predicting the most likely emojis that fit a particular short text has become an important and challenging task in both academia and industry. In this paper, we propose a hybrid sequence generation model, Seq2Emoji, to predict multiple emojis based on a short text. Seq2Emoji is an encoder–decoder model, in which we consider the correlations between emojis and take the emoji prediction task as a sequence generation problem. It extracts features through a hierarchical structure and self-attention mechanism and decodes them with a composite recurrent neural network before predicting emojis. During the prediction, Diverse Beam Search algorithm is also introduced to increase the diversity of predicted emojis. Experiments are carried out on our collected Weibo dataset (Chinese) and the results show that our proposed Seq2Emoji model is superior to the competitive models in both accuracy and diversity of emoji prediction.

Hindi English Mixed Text on Twitter Along with Emoji Prediction

This paper, explains socialite platform like Facebook to develop a corpus of Hindi English mixed language. An approach is presented by us to essence socialite microbe like chirp on Facebook. This paper includes Hindi English as a language to collect data from tweets. Moreover, many NLP tasks are analyzed and performed, make use of the corpus to get amazing results. In this paper, the methodology is applicable for resource deficient language provided that appropriate accent are alive users on socialite podium.

Multilingual emoji prediction using BERT for sentiment analysis

PurposeRecently, Unicode has been standardized with the penetration of social networking services, the use of emojis has become common. Emojis, as they are also known, are most effective in expressing emotions in sentences. Sentiment analysis in natural language processing manually labels emotions for sentences. The authors can predict sentiment using emoji of text posted on social media without labeling manually. The purpose of this paper is to propose a new model that learns from sentences using emojis as labels, collecting English and Japanese tweets from Twitter as the corpus. The authors verify and compare multiple models based on attention long short-term memory (LSTM) and convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT).Design/methodology/approachThe authors collected 2,661 kinds of emoji registered as Unicode characters from tweets using Twitter application programming interface. It is a total of 6,149,410 tweets in Japanese. First, the authors visualized a vector space produced by the emojis by Word2Vec. In addition, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. Second, it involves entering a line of tweets containing emojis, learning and testing with that emoji as a label. The authors compared the BERT model with the conventional models [CNN, FastText and Attention bidirectional long short-term memory (BiLSTM)] that were high scores in the previous study.FindingsVisualized the vector space of Word2Vec, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. The authors obtained a higher score with BERT models compared to the conventional model. Therefore, the sophisticated experiments demonstrate that they improved the score over the conventional model in two languages. General emoji prediction is greatly influenced by context. In addition, the score may be lowered due to a misunderstanding of meaning. By using BERT based on a bi-directional transformer, the authors can consider the context.Practical implicationsThe authors can find emoji in the output words by typing a word using an input method editor (IME). The current IME only considers the most latest inputted word, although it is possible to recommend emojis considering the context of the inputted sentence in this study. Therefore, the research can be used to improve IME performance in the future.Originality/valueIn the paper, the authors focus on multilingual emoji prediction. This is the first attempt of comparison at emoji prediction between Japanese and English. In addition, it is also the first attempt to use the BERT model based on the transformer for predicting limited emojis although the transformer is known to be effective for various NLP tasks. The authors found that a bidirectional transformer is suitable for emoji prediction.

An Active Learning Based Emoji Prediction Method in Turkish

Emoji usage has become a standard in social media platforms since it can condense feelings beyond short textual information. Recent advances in machine learning enable to write short messages with automatically detected emojis. However, the prediction of emojis for the given short message can be complicated, inasmuch as users can interpret different meanings beyond the intent of their designers. Therefore, an automatic extraction strategy of training samples cannot be convenient from the large volumes of unlabeled tweets. In this paper, we present an active learning method to evaluate the emoji prediction of a tweet with a limited number of labeled Turkish emoji dataset. To simulate a human-machine collaborative learning method, we train an initial classifier with this dataset and then we update the classifier by filtering related samples out from the large pool of unlabeled data. In the evaluation, we split 25\% randomly selected tweets combined with only one emoji from the generated dataset as a test case. Our active learning method has achieved 0.901 F1 score and outperforms other baseline supervised learning methods.

Emoji Identification and Prediction in Hebrew Political Corpus

Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment content than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendation for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks

Towards Understanding Creative Language in Tweets

Extracting fine-grained information from social media is traditionally a challenging task, since the language used in social media messages is usually informal, with creative genre-specific terminology and expression. How to handle such a challenge so as to automatically understand the opinions that people are communicating has become a hot subject of research. In this paper, we aim to show that leveraging the pre-learned knowledge can help neural network models understand the creative language in Tweets. In order to address this idea, we present a transfer learning model based on BERT. We fine-turned the pre-trained BERT model and applied the customized model to two downstream tasks described in SemEval-2018: Irony Detection task and Emoji Prediction task of Tweets. Our model could achieve an F-score of 38.52 (ranked 1/49) in Emoji Prediction task and 67.52 (ranked 2/43) and 51.35 (ranked 1/31) in Irony Detection subtask A and subtask B. The experimental results validate the effectiveness of our idea.