Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis

Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.

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

[2]  Erik Cambria,et al.  Multi-level Multiple Attentions for Contextual Multimodal Sentiment Analysis , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[3]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

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

[5]  Delip Rao,et al.  Semi-Supervised Polarity Lexicon Induction , 2009, EACL.

[6]  Roger Zimmermann,et al.  Self-Attentive Feature-Level Fusion for Multimodal Emotion Detection , 2018, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

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

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

[9]  Siu Cheung Hui,et al.  Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis , 2017, AAAI.

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

[11]  Cheng Li,et al.  Deep Memory Networks for Attitude Identification , 2017, WSDM.

[12]  Yang Gao,et al.  Compact Bilinear Pooling , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Verónica Pérez-Rosas,et al.  Learning Sentiment Lexicons in Spanish , 2012, LREC.

[14]  Erik Cambria,et al.  Tensor Fusion Network for Multimodal Sentiment Analysis , 2017, EMNLP.

[15]  Xiaocheng Feng,et al.  Effective LSTMs for Target-Dependent Sentiment Classification , 2015, COLING.

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