Are Emojis Predictable?

Barbieri, Francesco; Ballesteros, Miguel; Saggion, Horacio

doi:10.18653/v1/e17-2017

Cited by 107 publications

(120 citation statements)

References 20 publications

(17 reference statements)

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“…Given the proven effectiveness of recurrent neural networks in different tasks (Chung et al, 2014;Vinyals et al, 2015;Bahdanau et al, 2014, interalia), which also includes modeling of tweets (Dhingra et al, 2016;Barbieri et al, 2017), our Emote prediction model is based on RNNs, which are modeled to learn sequential data. We use the word based B-LSTM architecture by Barbieri et al (2017), designed to model emojis in Twitter.…”

Section: Bi-directional Lstmsmentioning

confidence: 99%

“…We use the word based B-LSTM architecture by Barbieri et al (2017), designed to model emojis in Twitter.…”

Section: Bi-directional Lstmsmentioning

confidence: 99%

“…Several recent works studied Emojis, focusing on emojis' semantics and usage (Aoki and Uchida, 2011;Barbieri et al, 2016a,b,c;Eisner et al, 2016;Ljubesic and Fiser, 2016;Ai et al, 2017;Miller et al, 2017), and sentiment (Novak et al, 2015;Hu et al, 2017). Finally, (Barbieri et al, 2017) presented an emoji prediction model for Twitter, where they use a char based B-LSTM to detect the 20 most frequent emojis.…”

Section: Related Workmentioning

confidence: 99%

“…Building up on previous research on predicting paralinguistic elements (e.g. emojis) (Barbieri et al, 2017), we target the Emote Prediction problem, i.e. the task of, given a collection of chatroom messages, predicting which emote the user is more likely to use.…”

Section: Introductionmentioning

confidence: 99%

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Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes

Barbieri¹,

Espinosa-Anke²,

Ballesteros³

et al. 2017

Proceedings of the 3rd Workshop on Noisy User-Generated Text

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Videogame streaming platforms have become a paramount example of noisy usergenerated text. These are websites where gaming is broadcasted, and allows interaction with viewers via integrated chatrooms. Probably the best known platform of this kind is Twitch, which has more than 100 million monthly viewers. Despite these numbers, and unlike other platforms featuring short messages (e.g. Twitter), Twitch has not received much attention from the Natural Language Processing community. In this paper we aim at bridging this gap by proposing two important tasks specific to the Twitch platform, namely (1) Emote prediction; and (2) Trolling detection. In our experiments, we evaluate three models: a BOW baseline, a logistic supervised classifiers based on word embeddings, and a bidirectional long short-term memory recurrent neural network (LSTM). Our results show that the LSTM model outperforms the other two models, where explicit features with proven effectiveness for similar tasks were encoded.

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Section: Bi-directional Lstmsmentioning

confidence: 99%

“…We use the word based B-LSTM architecture by Barbieri et al (2017), designed to model emojis in Twitter.…”

Section: Bi-directional Lstmsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes

Barbieri¹,

Espinosa-Anke²,

Ballesteros³

et al. 2017

Proceedings of the 3rd Workshop on Noisy User-Generated Text

Self Cite

View full text Add to dashboard Cite

show abstract

“…Social media platforms such as Twitter has accumulated a large number of emoji-incorporated messages. Analyzing the relationships between the textual message and emojis has many potential applications, such as emoji recommendation, automatic emoji-enriched message generation, and accurate sentiment analysis of social media messages (Barbieri et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction

Wu¹,

Wu²,

Wu³

et al. 2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets. We propose a residual CNN-LSTM with attention (RCLA) model for this task. Our model combines CNN and LSTM layers to capture both local and long-range contextual information for tweet representation. In addition, attention mechanism is used to select important components. Besides, residual connection is applied to CNN layers to facilitate the training of neural networks. We also incorporated additional features such as POS tags and sentiment features extracted from lexicons. Our model achieved 30.25% macro-averaged F-score in the first subtask (i.e., emoji prediction in English), ranking 7 th out of 48 participants.

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