Knowledge Graph Enhanced Aspect-Level Sentiment Analysis

• URL: http://arxiv.org/abs/2312.10048v3
• Date: Sat, 27 Jan 2024 00:09:23 GMT
• Title: Knowledge Graph Enhanced Aspect-Level Sentiment Analysis
• Authors: Kavita Sharma, Ritu Patel, Sunita Iyer
• Abstract summary: We propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms.
• Score: 1.342834401139078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms. Experiments on three widely used datasets demonstrate the superior performance of our method in sentiment classification.

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