Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture

TL;DR

This paper introduces a neural network architecture that improves aspect-based sentiment analysis by dividing the task into subtasks and combining specialized neural components, achieving state-of-the-art results on multiple datasets.

Contribution

It proposes a novel stacked neural network architecture for relation-based sentiment analysis, integrating components for aspect/opinion term extraction and relation classification.

Findings

Outperforms standard CNN and RNN architectures in aspect and opinion term extraction.

Achieves 18% accuracy improvement in sentiment labeling on USAGE dataset.

Sets new state-of-the-art in aspect-opinion relation extraction with 15% higher F-Measure.

Abstract

Sentiment analysis can be regarded as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme or event needs to be extracted. We present a novel neural architecture for sentiment analysis as a relation extraction problem that addresses this problem by dividing it into three subtasks: i) identification of aspect and opinion terms, ii) labeling of opinion terms with a sentiment, and iii) extraction of relations between opinion terms and aspect terms. For each subtask, we propose a neural network based component and combine all of them into a complete system for relational sentiment analysis. The component for aspect and opinion term extraction is a hybrid architecture consisting of a recurrent neural network stacked on top of a convolutional neural network. This approach outperforms a standard convolutional deep neural architecture as well as a recurrent network architecture and performs competitively compared to other methods on two datasets of annotated customer reviews. To extract sentiments for individual opinion terms, we propose a recurrent architecture in combination with word distance features and achieve promising results, outperforming a majority baseline by 18% accuracy and providing the first results for the USAGE dataset. Our relation extraction component outperforms the current state-of-the-art in aspect-opinion relation extraction by 15% F-Measure.