Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification
This work addresses sentiment analysis for natural language processing applications, but it is incremental as it combines existing methods like GRU and CNN attention.
The paper tackled sentiment classification tasks at SemEval 2018 by developing a system with task-specific word embeddings, a GRU neural network with a CNN attention mechanism, and stacking ensembles, achieving 3rd place in English and 1st place in Spanish valence ordinal classification.
This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system consists of three parts: training task-specific word embeddings, training a model consisting of gated-recurrent-units (GRU) with a convolution neural network (CNN) attention mechanism and training stacking-based ensembles for each of the sub-tasks. Our algorithm reached 3rd and 1st places in the valence ordinal classification sub-tasks in English and Spanish, respectively.