Graph-Based Attention Networks for Aspect Level Sentiment Analysis

With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we present a deep learning model that employs graph neural networks and graph-based attention mechanisms for aspect based sentiment analysis. In our work, the given text is considered as a graph based on its syntactic structure and the target is the specific region of the graph. Structural attention model and graph attention model are used to concentrate on relations between words and certain regions of the graph. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines. Code is available in supplementary materials.

[1]  Yue Zhang,et al.  Gated Neural Networks for Targeted Sentiment Analysis , 2016, AAAI.

[2]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[3]  Ming Zhou,et al.  Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification , 2014, ACL.

[4]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[5]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[6]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[7]  Kam-Fai Wong,et al.  Convolution-based Memory Network for Aspect-based Sentiment Analysis , 2018, SIGIR.

[8]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[9]  Edouard Pineau,et al.  A Simple Baseline Algorithm for Graph Classification , 2018, ArXiv.

[10]  Qiao Liu,et al.  Content Attention Model for Aspect Based Sentiment Analysis , 2018, WWW.

[11]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[12]  Li Zhao,et al.  Attention-based LSTM for Aspect-level Sentiment Classification , 2016, EMNLP.

[13]  Diego Marcheggiani,et al.  Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks , 2018, NAACL.

[14]  Samuel S. Schoenholz,et al.  Neural Message Passing for Quantum Chemistry , 2017, ICML.

[15]  Joachim Wagner,et al.  DCU: Aspect-based Polarity Classification for SemEval Task 4 , 2014, *SEMEVAL.

[16]  Houfeng Wang,et al.  Interactive Attention Networks for Aspect-Level Sentiment Classification , 2017, IJCAI.

[17]  Diego Marcheggiani,et al.  Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling , 2017, EMNLP.

[18]  Xiaokui Xiao,et al.  Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis , 2016, EMNLP.

[19]  Suresh Manandhar,et al.  SemEval-2014 Task 4: Aspect Based Sentiment Analysis , 2014, *SEMEVAL.

[20]  Yue Zhang,et al.  Target-Dependent Twitter Sentiment Classification with Rich Automatic Features , 2015, IJCAI.

[21]  Ting Liu,et al.  Aspect Level Sentiment Classification with Deep Memory Network , 2016, EMNLP.

[22]  Diego Marcheggiani,et al.  A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling , 2017, CoNLL.

[23]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[24]  Kiyoaki Shirai,et al.  PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis , 2015, EMNLP.

[25]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[26]  Ryan A. Rossi,et al.  Deep Graph Attention Model , 2017, ArXiv.

[27]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[28]  Saif Mohammad,et al.  NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews , 2014, *SEMEVAL.

[29]  Khalil Sima'an,et al.  Graph Convolutional Encoders for Syntax-aware Neural Machine Translation , 2017, EMNLP.

[30]  Tao Li,et al.  Aspect Based Sentiment Analysis with Gated Convolutional Networks , 2018, ACL.

[31]  Jeonghee Yi,et al.  Sentiment analysis: capturing favorability using natural language processing , 2003, K-CAP '03.

[32]  Richard Socher,et al.  Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning , 2014 .

[33]  Xin Li,et al.  Transformation Networks for Target-Oriented Sentiment Classification , 2018, ACL.

[34]  Xiaocheng Feng,et al.  Target-Dependent Sentiment Classification with Long Short Term Memory , 2015, ArXiv.

[35]  Ming Zhou,et al.  Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification , 2014, ACL.

[36]  Ryan A. Rossi,et al.  Graph Classification using Structural Attention , 2018, KDD.

[37]  Lishuang Li,et al.  Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis , 2018, CoNLL.

[38]  Lidong Bing,et al.  Recurrent Attention Network on Memory for Aspect Sentiment Analysis , 2017, EMNLP.

[39]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[40]  Claire Cardie,et al.  39. Opinion mining and sentiment analysis , 2014 .