Recurrent Attention Network on Memory for Aspect Sentiment Analysis
Peng Chen, Zhongqian Sun, Lidong Bing, Wei Ping Yang · 2017
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information.The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications.The weightedmemory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence.We examine the merit of our model on four datasets: two are from Se-mEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity.The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.