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Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment

Xianyong Li¹, Li Ding¹, Yajun Du¹

¹School of Computer and Software Engineering, Xihua University, Chengdu, China.

Frontiers in Psychology

|February 11, 2022

Summary

This study introduces a novel position-enhanced network for aspect-level sentiment classification (ASC). The proposed model significantly improves accuracy by incorporating positional information, outperforming existing methods.

Keywords:

BiGRU aspect terms aspect-level sentiment classification attention mechanism long short term memory networks position information

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Area of Science:

Natural Language Processing
Machine Learning
Artificial Intelligence

Background:

Aspect-level sentiment classification (ASC) is crucial for fine-grained opinion analysis.
Existing attention-based methods often overlook the importance of word proximity to the aspect.

Purpose of the Study:

To propose a novel model that integrates positional information into attention mechanisms for improved ASC.
To enhance the performance of sentiment analysis by considering the influence of word distance.

Main Methods:

A position influence vector was designed to capture the relationship between aspect and context words.
A position-enhanced multi-head self-attention network based BiGRU (PMHSAT-BiGRU) was developed.
The model combines position influence vectors, multi-head self-attention, and BiGRU.

Main Results:

The PMHSAT-BiGRU model demonstrated superior performance across multiple benchmark datasets (SemEval2014/2015/2016).
Accuracy improvements over baseline models, including LSTM, were significant, ranging from 3.15% to 6.06%.

Conclusions:

The proposed PMHSAT-BiGRU model effectively leverages positional information for enhanced aspect-level sentiment classification.
Integrating positional awareness into attention mechanisms offers a promising direction for advancing sentiment analysis research.