Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

TL;DR

This paper presents a two-step neural network architecture for aspect-based sentiment analysis, effectively extracting aspects and their sentiments from unstructured text using pretrained embeddings and semantic knowledge.

Contribution

It introduces a flexible two-stage neural approach that independently handles aspect extraction and sentiment classification, enhanced with semantic features from WordNet and SenticNet.

Findings

Achieved top performance in ESWC-2016 Challenge category

Demonstrated effectiveness of semantic knowledge integration

Validated the approach's flexibility and accuracy

Abstract

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis.