Recurrent Attention Network on Memory for Aspect Sentiment Analysis

EMNLP 2017  ·  Peng Chen, Zhongqian Sun, Lidong Bing, Wei Yang ·

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 weighted-memory 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 SemEval2014, 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. read more

PDF Abstract
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Aspect-Based Sentiment Analysis SemEval 2014 Task 4 Sub Task 2 RAM Restaurant (Acc) 80.23 # 29
Laptop (Acc) 74.49 # 24
Mean Acc (Restaurant + Laptop) 77.36 # 24

Methods


No methods listed for this paper. Add relevant methods here