A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis (1609.02745v1)

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.

• The paper introduces a hierarchical H-LSTM that integrates intra- and inter-sentence relationships for more accurate aspect-based sentiment analysis.
• The model outperforms traditional CNN and LSTM baselines, demonstrating robust performance on multilingual and multi-domain datasets.
• The research highlights potential for domain adaptation and low-resource language integration, opening avenues for enhanced real-world sentiment analysis.

A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis

The paper "A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis" by Sebastian Ruder, Parsa Ghaffari, and John G. Breslin presents a compelling approach for the task of aspect-based sentiment analysis (ABSA) using a hierarchical bidirectional LSTM (H-LSTM) model. The research focuses on overcoming deficiencies in traditional sentiment analysis methodologies that treat review sentences independently, without considering the interdependencies that comprise the argumentative structure of reviews.

Model Overview and Methodology

The core of the paper proposes a hierarchical model that captures both intra- and inter-sentence relationships within reviews, exploiting these for improved sentiment classification. This model divergence from prior approaches is its ability to harness the structural dependencies found naturally in reviews, thus providing a more holistic analysis of sentiment.

The authors introduce a bidirectional LSTM architecture, layered hierarchically. The sentence-level LSTMs are tasked with processing individual sentences, while the review-level LSTM engages with the outputs of the sentence-level LSTMs along with aspect embeddings. This hierarchical design choice mirrors the complex nature of review discourse, often structured according to Rhetorical Structure Theory (RST). The aspect representation serves to isolate sentiment concerning specific facets of a product or service, such as quality, price, or usability, in various contexts and language settings.

Datasets and Results

The experiments are executed on a diverse set of datasets covering eight languages and five domains, including SemEval-2016 ABSA task data. Results substantiate the adoption of the hierarchical model as significantly more effective compared to sentence-level baselines like CNN and LSTM when considering the review context. Particularly compelling is the model's outperformance of state-of-the-art approaches on five datasets spanning multilingual and multi-domain datasets, which demonstrates its robust efficacy in scenarios devoid of hand-crafted features or lexica.

Implications and Analysis

The research not only achieves notable improvements in ABSA but also illustrates the potential for exploration in multilingual and low-resource language settings. Due to its learning from the structure of reviews themselves, this approach circumvents the complexities and limitations inherent in bespoke lexicon usage or manual feature engineering. This independence is attributed to the model’s effectiveness in situations where traditional resources are sparse or unavailable. The use of pre-trained GloVe embeddings additionally aids in enhancing performance, though its impact varies by language.

Future Directions

Looking forward, the paper suggests exploring the integration of domain-specific information and resources to bolster sentiment models. The ability to seamlessly incorporate sentiment lexicons and exploit contextual cues at a more nuanced level could potentially drive further performance gains. Additionally, the extension of hierarchical models to other aspects of natural language processing tasks where document structure plays a critical role could be a fertile area for future research.

In conclusion, the hierarchical LSTM model for ABSA proposed by Ruder et al. represents a significant advancement in harnessing the argumentative structure of reviews for sentiment analysis. Its implications for multilingual sentiment analysis and applications in real-world datasets offer exciting avenues for further exploration within the field of AI and natural language processing.