Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.

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

[2]  Haris Papageorgiou,et al.  SemEval-2016 Task 5: Aspect Based Sentiment Analysis , 2016, *SEMEVAL.

[3]  Yangqiu Song,et al.  Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision , 2019, ACL.

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

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

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

[7]  Xiaokui Xiao,et al.  Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms , 2017, AAAI.

[8]  Xin Li,et al.  A Unified Model for Opinion Target Extraction and Target Sentiment Prediction , 2018, AAAI.

[9]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[10]  Luo Si,et al.  Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis , 2019, AAAI.

[11]  Alexander M. Rush,et al.  Structured Attention Networks , 2017, ICLR.

[12]  Yue Zhang,et al.  Attention Modeling for Targeted Sentiment , 2017, EACL.

[13]  Honglei Guo,et al.  Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis , 2019, CoNLL.

[14]  Hwee Tou Ng,et al.  An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis , 2019, ACL.

[15]  Hwee Tou Ng,et al.  An Unsupervised Neural Attention Model for Aspect Extraction , 2017, ACL.

[16]  Hao Li,et al.  Learning Explicit and Implicit Structures for Targeted Sentiment Analysis , 2019, EMNLP/IJCNLP.

[17]  Jiebo Luo,et al.  Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis , 2019, ACL.

[18]  Lu Xu,et al.  Position-Aware Tagging for Aspect Sentiment Triplet Extraction , 2020, EMNLP.

[19]  Min Yang,et al.  Attention Based LSTM for Target Dependent Sentiment Classification , 2017, AAAI.

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

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

[22]  Philip S. Yu,et al.  Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction , 2018, ACL.

[23]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

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

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

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

[27]  Qiang Yang,et al.  Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification , 2018 .

[28]  Hwee Tou Ng,et al.  Effective Attention Modeling for Aspect-Level Sentiment Classification , 2018, COLING.

[29]  Alberto Costa,et al.  RBFOpt: an open-source library for black-box optimization with costly function evaluations , 2018, Mathematical Programming Computation.

[30]  Hao Li,et al.  Learning Explicit and Implicit Structures for Targeted Sentiment Analysis , 2019 .

[31]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[32]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[33]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[34]  Wei Lu,et al.  Learning Latent Opinions for Aspect-level Sentiment Classification , 2018, AAAI.

[35]  Erik Cambria,et al.  Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM , 2018, AAAI.

[36]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[37]  Qiang Yang,et al.  Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning , 2019, EMNLP.

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

[39]  Chen Zhang,et al.  Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks , 2019, EMNLP/IJCNLP.

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

[41]  Jun Zhao,et al.  Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews , 2013, ACL.

[42]  Xin Li,et al.  Aspect Term Extraction with History Attention and Selective Transformation , 2018, IJCAI.