Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation: A Novel Approach for Improved Aspect-Based Sentiment Analysis

Core Concepts

Aspect sentiment coherency is a prevalent yet underexplored phenomenon in aspect-based sentiment analysis, where adjacent aspects often share similar sentiments. This work proposes a novel local sentiment aggregation (LSA) paradigm to effectively model aspect sentiment coherency, leading to significant improvements in aspect sentiment classification performance.

Abstract

The paper introduces the concept of aspect sentiment coherency, which refers to the common pattern where adjacent aspects often share similar sentiments. This phenomenon has been overlooked in existing research, despite its prevalence.
To address this, the authors propose a novel local sentiment aggregation (LSA) paradigm. LSA constructs a differential-weighted sentiment aggregation window based on various aspect-specific features to guide the modeling of aspect sentiment coherency. Three variants of LSA are introduced, namely LSAP, LSAT, and LSAS, each utilizing different aspect feature representations.
The authors conduct extensive experiments to evaluate the performance of LSA in aspect sentiment coherency extraction and traditional aspect sentiment classification. The results demonstrate that LSA significantly outperforms existing state-of-the-art models in both tasks, setting new benchmarks across five public datasets. The authors also showcase LSA's promising ability in defending against adversarial attacks in aspect-based sentiment analysis.
Furthermore, the authors provide a case study to validate LSA's capability in learning local sentiment coherency. The results highlight LSA's effectiveness in identifying aspect sentiment clusters and coherent sentiments, even in the presence of adverse sentiment aggregation.
Overall, this work offers a new perspective on aspect-based sentiment analysis by introducing the concept of aspect sentiment coherency and proposing an efficient and effective solution to model it, leading to substantial improvements in sentiment analysis performance.

Stats

The laptop has a lot of storage.
The battery capacity is also good.
This laptop has a lot of storage, and so does the battery capacity.
The speakers of this TV is great! Just like its screen.
The speakers of this TV sucks! Just like its screen.

Quotes

"Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification."
"Modeling aspect sentiment coherency can provide valuable insights."
"LSA achieves impressive performance in aspect sentiment coherency extraction and sentiment classification, setting new state-of-the-art results on five widely-used datasets based on the latest DeBERTa (He et al., 2021) model."

Key Insights Distilled From

LSA

by Heng Yang,Ke... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2110.08604.pdf

Deeper Inquiries

How can the sentiment aggregation window construction in LSA be further improved to capture more nuanced sentiment coherency patterns?

In order to enhance the sentiment aggregation window construction in LSA to capture more nuanced sentiment coherency patterns, several strategies can be considered. One approach could involve incorporating more advanced natural language processing techniques, such as syntactic and semantic parsing, to better understand the relationships between aspects and sentiments. By leveraging more sophisticated feature extraction methods, LSA can potentially capture subtle nuances in sentiment coherency. Additionally, exploring the use of attention mechanisms or reinforcement learning to dynamically adjust the sentiment aggregation window based on the context could further improve the model's ability to capture complex sentiment patterns.

What are the potential limitations of the current LSA approach, and how can it be extended to handle more complex aspect-based sentiment analysis scenarios?

One potential limitation of the current LSA approach is its reliance on basic aspect features for sentiment aggregation, which may not capture the full complexity of sentiment coherency. To address this limitation and handle more complex aspect-based sentiment analysis scenarios, LSA can be extended in several ways. Firstly, incorporating more advanced feature extraction techniques, such as contextual embeddings or syntactic information, can help capture the intricate relationships between aspects and sentiments. Additionally, integrating multi-task learning or transfer learning approaches to leverage pre-trained models for sentiment analysis can enhance the model's performance on diverse datasets and scenarios. Furthermore, exploring ensemble methods or hybrid models that combine LSA with other sentiment analysis techniques can provide a more comprehensive and robust solution for handling complex sentiment analysis tasks.

Given the success of LSA in aspect-based sentiment analysis, how can the concept of sentiment coherency be applied to other text analysis tasks, such as document-level sentiment analysis or emotion recognition?

The concept of sentiment coherency, as demonstrated by LSA in aspect-based sentiment analysis, can be applied to other text analysis tasks such as document-level sentiment analysis or emotion recognition to improve the understanding of sentiment patterns in a broader context. In document-level sentiment analysis, sentiment coherency can help identify consistent sentiment trends across different sections of a document or identify conflicting sentiments within the text. By incorporating sentiment coherency modeling techniques similar to LSA, document-level sentiment analysis models can provide more accurate and nuanced sentiment predictions. Similarly, in emotion recognition tasks, sentiment coherency can aid in understanding the relationships between different emotions expressed in a text and capturing the overall emotional tone of the content. By leveraging sentiment coherency modeling approaches, emotion recognition models can better capture the complex interplay of emotions and sentiments in text data, leading to more precise emotion classification results.