A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis

TL;DR

This paper introduces a hierarchical bidirectional LSTM model that captures the structure of reviews to improve aspect-based sentiment analysis, outperforming existing methods across multiple datasets without relying on hand-engineered features.

Contribution

It presents a novel hierarchical model that leverages review structure for sentiment analysis, achieving state-of-the-art results without external resources.

Findings

Hierarchical model outperforms non-hierarchical baselines.

Achieves competitive results with state-of-the-art methods.

Outperforms on five multilingual, multi-domain datasets.

Abstract

Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review's argumentative structure. Intuitively, sentences in a review build and elaborate upon each other; knowledge of the review structure and sentential context should thus inform the classification of each sentence. We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM. We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain datasets without any hand-engineered features or external resources.