SwissCheese: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision
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Date
2016-06
Publication Type
Conference Paper
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yes
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Abstract
In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose predictions are combined using a random forest classifier. Our approach was evaluated on the datasets of the SemEval-2016 competition (Task 4) outperforming all otherapproaches for the Message Polarity Classification task.
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published
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Book title
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
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Pages / Article No.
1124 - 1128
Publisher
Association for Computer Linguistics
Event
10th International Workshop on Semantic Evaluation (SemEval-2016)
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Organisational unit
09462 - Hofmann, Thomas / Hofmann, Thomas