Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document with respect to a given aspect entity. While neural network architectures have been successful in predicting the overall polarity of sentences, aspect-specific sentiment analysis still remains as an open problem. In this paper, we propose a novel method for integrating aspect information into the neural model. More specifically, we incorporate aspect information into the neural model by modeling word-aspect relationships. Our novel model, \textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term. This ameliorates the flaws of other state-of-the-art models that utilize naive concatenations to model word-aspect similarity. Instead, our model adopts circular convolution and circular correlation to model the similarity between aspect and words and elegantly incorporates this within a differentiable neural attention framework. Finally, our model is end-to-end differentiable and highly related to convolution-correlation (holographic like) memories. Our proposed neural model achieves state-of-the-art performance on benchmark datasets, outperforming ATAE-LSTM by $4\%-5\%$ on average across multiple datasets.

Accepted to AAAI2018

References in corpus (4)

• Adam: A Method for Stochastic Optimization
• Reasoning about Entailment with Neural Attention
• Quasi-Recurrent Neural Networks
• Associative Long Short-Term Memory

Cited by in corpus (8)

• A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
• Transformation Networks for Target-Oriented Sentiment Classification
• Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
• Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
• CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis
• Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
• Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis
• Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis