A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 6280–6285, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 6280 A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis Qingnan Jiang1, Lei Chen1, Ruifeng Xu2,3, Xiang Ao4, Min Yang1∗ 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 2Department of Computer Science, Harbin Institute of Technology (Shenzhen) 3Peng Cheng Laboratory 4Institute of Computing Technology, Chinese Academy of Sciences jqnthomask@gmail.com, lei.chen@siat.ac.cn, xuruifeng@hit.edu.cn aoxiang@ict.ac.cn, min.yang@siat.ac.cn Abstract Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications. In existing ABSA datasets, most sentences contain only one as- pect or multiple aspects with the same senti- ment polarity, which makes ABSA task degen- erate to sentence-level sentiment analysis. In this paper, we present a new large-scale Multi- Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two dif- ferent aspects with different sentiment polar- ities. The release of this dataset would push forward the research in this field. In addi- tion, we propose simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances. Ex- periments on our new dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods1. 1 Introduction Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity towards the spe- cific aspect in a sentence. An target aspect refers to a word or a phrase describing an aspect of an entity. For example, in the sentence “The decor is not special at all but their amazing food makes up for it”, there are two aspect terms “decor” and “food”, and they are associated with negative and positive sentiment respectively. Recently, neural network methods have domi- nated the study of ABSA since these methods can be trained end-to-end and automatically learn im- portant features. (Wang et al., 2016) proposed to learn an embedding vector for each aspect, and these aspect embeddings were used to calculate the attention weights to capture important infor- mation with regard to the given aspects. (Tang ∗Min Yang is corresponding author 1Data and code can be found as: https://github.com/siat- nlp/MAMS-for-ABSA et al., 2016b) developed the deep memory network to compute the importance degree and text repre- sentation of each context word with multiple at- tention layers. (Ma et al., 2017) introduced the in- teractive attention networks (IAN) to interactively learn attentions in contexts and targets, and gen- erated the representations for target and context words separately. (Xue and Li, 2018) proposed to extract sentiment features with convolutional neu- ral networks and selectively output aspect related features for classification with gating mechanisms. Subsequently, Transformer (Vaswani et al., 2017) and BERT based methods (Devlin et al., 2018) have shown high potentials on ABSA task. There are also several studies attempting to simulate the process of human reading cognition to further im- prove the performance of ABSA (Lei et al., 2019; Yang et al., 2019). So far, several ABSA datasets have …

Content truncated to 500 words