Simon Müller

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2017

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TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision
Simon Müller | Tobias Huonder | Jan Deriu | Mark Cieliebak
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN.With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place.
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