Content

Train.csv contains tweets and labels are emojis. You can find the emoji-label mapping in Mapping.csv. Predict emoji's to use for the test set.

Approaches

Best method among those tried was Bi-directional LSTM with Glove embeddings (42B)

License

Belongs to the original author on Twitter

This dataset contains 100d word embeddings trained on 48M Italian tweets using fastText and employed by our team to predict emojis during ITAmoji competition of EVALITA 2018 Evaluation Campaign.

The twitter emoji dataset obtained from CodaLab comprises of 50 thousand tweets along with the associated emoji label. Each tweet in the dataset has a corresponding numerical label which maps to a specific emoji. The emojis are of the 20 most frequent emojis and hence the labels range from 0 to 19

This dataset is created by leveraging the social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). We created corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.

IntroductionThe worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward COVID-19 expressed on social media, specifically Twitter, can be used to enhance COVID-19 case prediction system performance.MethodsWe use emoji as a proxy to shallowly capture the trend in emotion expression on Twitter. Two aspects of emoji are studied: the surface trend in emoji usage by using the tweet count and the structural interaction of emoji by using an anomalous score.ResultsOur experimental results show that utilizing emoji improved system performance in the majority of evaluations.