DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets

2018-06-01 · SEMEVAL 2018 6 · Dimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis

This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team. First, we employed a combination of pre-processing techniques to reduce the noise of tweets and produce a number of features. Then, we built several N-grams, to represent the combination of word and emojis. Finally, we trained our system with a tuned LinearSVC classifier. Our approach in the leaderboard ranked 18th amongst 48 teams.

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Tasks

Information RetrievalTask 2

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

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In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression.…

General Classification Information Retrieval regression Sentiment Analysis+2

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Medical Diagnosis Prediction Task 2

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In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict the most likely associated emoji in Eng…

Task 2 Word Embeddings

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Position Prediction

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This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural N…

Task 2