Abstract
This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Soft- max on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4% average recall.
Cite
CITATION STYLE
Hamdan, H. (2017). Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 700–703). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s17-2116
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