Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture
This work addresses sentiment analysis as a relation extraction problem, offering a novel method for extracting opinions and aspects from customer reviews, with incremental improvements in specific tasks.
The paper tackles aspect-based relational sentiment analysis by dividing it into three subtasks and proposes a stacked neural network architecture, achieving competitive results such as outperforming the state-of-the-art in relation extraction by 15% F-Measure and a majority baseline by 18% accuracy.
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.