Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks
Lecture notes in computer sciencePublished 1 January 2018
Binxuan Huang, Yanglan Ou, Kathleen M. Carley
Citations348
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper introduces an attention-over-attention (AOA) neural network for aspect level sentiment classification and demonstrates the approach outperforms previous LSTM-based architectures.
Abstract
Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences. In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification. Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences. With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences. Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.
Keywords
Computer Science
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