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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