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Main Authors: Sharma, Kavita, Patel, Ritu, Iyer, Sunita
Format: Preprint
Published: 2023
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Online Access:https://arxiv.org/abs/2312.10048
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author Sharma, Kavita
Patel, Ritu
Iyer, Sunita
author_facet Sharma, Kavita
Patel, Ritu
Iyer, Sunita
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2312_10048
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Knowledge Graph Enhanced Aspect-Level Sentiment Analysis
Sharma, Kavita
Patel, Ritu
Iyer, Sunita
Computation and Language
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.
title Knowledge Graph Enhanced Aspect-Level Sentiment Analysis
topic Computation and Language
url https://arxiv.org/abs/2312.10048