SemEval 2018 Task 2: Multilingual Emoji Prediction

Barbieri, Francesco; Camacho-Collados, José; Ronzano, Francesco; Espinosa-Anke, Luis; Ballesteros, Miguel; Basile, Valerio; Patti, Viviana; Saggion, Horacio

doi:10.18653/v1/s18-1003

Cited by 139 publications

(101 citation statements)

References 40 publications

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“…The model was evaluated on the English data provided for the SemEval 2018 Shared Task on Emoji Prediction. 84 It classified emojis into 20 classes and showed improved results in comparison with baseline FastText method. 85 Using a bidirectional transformer-based BERT Architecture, Aditya Malte and Pratik Ratadiya 86 detected cyber abuse from Facebook multilingual texts, English, Hindi, and a mixture of both languages (Hinglish) texts.…”

Section: Current State-of-the-art Text-based Proposalsmentioning

confidence: 99%

“…It involved a two stacked word‐based bidirectional LSTM networks 83 with skip connections between the first and the second LSTM and an attention layer to focus on important input features. The model was evaluated on the English data provided for the SemEval 2018 Shared Task on Emoji Prediction 84 . It classified emojis into 20 classes and showed improved results in comparison with baseline FastText method 85 …”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

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Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

235

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Current State-of-the-art Text-based Proposalsmentioning

confidence: 99%

Section: Detection Approaches and Related Workmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

235

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“…In this section, we report the obtained results by our model according to the metric evaluation of the challenge, macro f1, precision and recall, accuracy, and f1 for all the emojis (Barbieri et al, 2018). Results are reported for five diverse configurations: (i) the system based on word embeddings and baf-of-words with Logistic Regression (LR); (ii) the system based on word embeddings and baf-of-words with Support Vector Machine (SVM); (iii) the bag-of-words system with Logistic Regression (LR); (iv) the bag-of-words system with Support Vector Machine (SVM); and (v) the bag-of-words system with Random Forest (RF).…”

Section: Resultsmentioning

confidence: 99%

“…This configuration was employed and evaluated in the SemEval 2018 challenge (task 2, subtask 1), in which the goal is to predict the emoji of a tweet (Barbieri et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets

Ribeiro

Silva

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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“…Twitter. SemEval-2018 Task 2 [16] introduced an Emoji Predication Task. Given a text message including an emoji, the goal is to predict that emoji based exclusively on the textual content of that message.…”

Section: Task 1: Emoji Predictionmentioning

confidence: 99%

Towards Understanding Creative Language in Tweets

Zhang¹,

Zhou²,

Yu³

et al. 2019

JSEA

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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.

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SemEval 2018 Task 2: Multilingual Emoji Prediction

Cited by 139 publications

References 40 publications

Text‐based emotion detection: Advances, challenges, and opportunities

Text‐based emotion detection: Advances, challenges, and opportunities

#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets

Towards Understanding Creative Language in Tweets

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