Emoji recommendation in private instant messages

Guibon, Gaël; Ochs, Magalie; Bellot, Patrice

doi:10.1145/3167132.3167430

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…Kralj Novak et al [15] proposed Emoji Sentiment Ranking for automated sentiment analysis; the analyses showed the important roles of the emoji icons for sentiment distribution in tweet and there are no significant differences in the emoji icons rankings between 13 European languages. Guibon et al [16] introduced approaches exploiting emoji icons in sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

2022

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The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low-resource languages. In this paper, we experiment and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax-tree transformation, and embedding mixup. Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data.

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Section: Related Workmentioning

confidence: 99%

Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

2022

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“…Felbo (Felbo et al, 2017) tackled emoji prediction by LSTM with 43.8% accuracy for the top 5 emojis, while using emoji vectors to help detect sarcasm. In our recent work we considered another approach with 84.48% weighted F1-score using multi-label emoji prediction of 169 sentiment related emojis in real private messages (Guibon et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction

Guibon¹,

Ochs²,

Bellot³

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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“…Neural models have been used to address the multi-label setting of the emoji prediction task as well. An automatic recommendation system based on user message analysis and real emoji usage was developed by Guibon, Ochs, and Bellot (2018). They showed that a Random Forest multi-label classifier with a bag-of-words/characters representation and calculated features outperforms (Barbieri et al, 2017) BLSTM networks.…”

Section: Introductionmentioning

confidence: 99%

Emoji Identification and Prediction in Hebrew Political Corpus

Liebeskind

2019

IISIT

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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

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Emoji recommendation in private instant messages

Cited by 13 publications

References 17 publications

Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction

Emoji Identification and Prediction in Hebrew Political Corpus

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