PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis

By Thien Hai Nguyen, Kiyoaki Shirai. 2015

244 citations recorded by the source corpus.

This paper presents a new method to identify sentiment of an aspect of an entity. It is an extension of RNN (Recursive Neural Network) that takes both dependency and constituent trees of a sentence into account. Results of an experiment show that our method significantly outperforms previous methods.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Erik Cambria
2. Lidong Bing
3. Xipeng Qiu
4. Ting-Wei Liu
5. Bing Qin
6. Duyu Tang
7. Soujanya Poria
8. Jiwei Li

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “ontent expresses a positive or negative attitude from the generator of the link to the recipient [14], and all such sentiment links form a new network topology called sentiment network. Previous work [6, 11, 15] mainly focuses on sentiment classification based on the concrete content posted by users”
• Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (2019, methodology): “The earliest work on (T)ABSA relied heavily on feature engineering (Wagner et al., 2014; Kiritchenko et al., 2014), and subsequent neural network-based methods (Nguyen and Shirai, 2015; Wang et al., 2016; Tang et al., 2015, 2016; Wang et al., 2017) achieved higher accuracy.”
• Aspect Level Sentiment Classification with Deep Memory Network (2016, methodology): “Representative approaches in literature include feature based Support Vector Machine (Kiritchenko et al., 2014; Wagner et al., 2014) and neural network models (Dong et al., 2014; Lakkaraju et al., 2014; Vo and Zhang, 2015; Nguyen and Shirai, 2015; Tang et al., 2015a).”
• Target-Dependent Sentiment Classification With BERT (2019, background): “Recursive neural networks [6], [23], gated neural networks [47], [49] and convolutional neural networks [13] are less popular.”
• Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (2019, background): “The ‘Sentiment’ circle linked to aspect terms refers to ATC which attracts a heated research popularity (Dong et al. 2014; Tang, Qin, and Liu 2016; Nguyen and Shirai 2015; Wang et al. 2016b; Ma et al. 2017; Tay, Luu, and Hui 2017; Ma, Peng, and Cambria 2018; Hazarika et al. 2018; Li et al. 2018a; Wa”
• INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis (2016, methodology): “However, without relying on dependency and constituency trees (Nguyen and Shirai, 2015) or positional information (Tang et al., 2015”
• Description Based Text Classification with Reinforcement Learning (2020, background): “…al., 2011; Socher et al., 2013; Tang et al., 2014, 2015b), aspect sentiment clas-sification ( Jo and Oh, 2011; Tang et al., 2015a; Wang et al., 2015; Nguyen and Shirai, 2015; Tang et al., 2016b; Pontiki et al., 2016; Sun et al., 2019a), topic classification (Schwartz et al., 1997; Quercia et al.,…”
• Recurrent Attention Network on Memory for Aspect Sentiment Analysis (2017, methodology): “(Dong et al., 2014; Nguyen and Shirai, 2015) adopted Rec-NN for aspect sentiment classification, by converting the opinion target as the tree root and propagating the sentiment of targets depending on the context and syntactic relationships between them.”
• Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (2019, background): “Some early work also tries to leverage the syntax structure (Dong et al., 2014; Nguyen and Shirai, 2015).”
• Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020, methodology): “Lakkaraju et al. (2014) used a recursive neural network (RNN) to hierarchically encode word representations and to jointly extract aspects and sen-timents.”
• Multi-grained Attention Network for Aspect-Level Sentiment Classification (2018, background): “Recursive Neural Network (Socher et al., 2011; Dong et al., 2014; Nguyen and Shirai, 2015) are studied to conduct semantic compositions on tree structures, and generate representations for prediction.”
• Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, methodology): “Deep learning methods (Nguyen and Shirai 2015; Wang et al. 2016; Tang et al. 2016; Tang, Qin, and Liu 2016; Wang et al. 2017) have achieved great accuracy when applied to ABSA and targeted sentiment analysis.”