The paper describes our submission to the task on Sentiment Analysis on Twitter at SemEval 2016. The approach is based on a Deep Learning architecture using convolu- tional neural networks. The approach used on- ly word embeddings as features. The submis- sion used embeddings created from a corpus of news articles. We report on further experi- ments using embeddings built for a corpus of tweets as well as sentiment specific word em- beddings obtained by distant supervision.

UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sen- timent Classification

Giuseppe Attardi
Co-primo
;
Daniele Sartiano
Co-primo
2016-01-01

Abstract

The paper describes our submission to the task on Sentiment Analysis on Twitter at SemEval 2016. The approach is based on a Deep Learning architecture using convolu- tional neural networks. The approach used on- ly word embeddings as features. The submis- sion used embeddings created from a corpus of news articles. We report on further experi- ments using embeddings built for a corpus of tweets as well as sentiment specific word em- beddings obtained by distant supervision.
978-1-941643-95-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/904486
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