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New feature attribution method for explainable aspect-based sentiment classification
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AI
DOI：10.1016/j.knosys.2024.112550.png)
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Abstract
En 中文
Recently, the use of feature attribution methods in explainable artificial intelligence has attracted significant attention. While many proposed methods in this domain involve analyzing the impact on results by filtering out specific features. Although these methods can provide explanations, they often overlook a relevant factor: the deletion of features alters the structural information of the text, resulting in the loss of relations between features, thus leading to explanations that are not comprehensive and appropriate. To address these challenges, this study introduces a novel feature attribution method known as General Explainable Sentiment Classification (GESC). Unlike traditional approaches that delete features, GESC focuses on preserving the inherent relations by substituting the features with synonyms. Furthermore, the GESC method employs the binary tumbleweed algorithm (BTA) to identify the optimal feature combination. Moreover, designers have created an adaptive iterative technique to accommodate texts of varying lengths. The application of GESC to aspect-based sentiment classification demonstrates its practicability. The experimental results indicate that the BTA primarily improves the efficiency of finding the optimal combination of features compared with other algorithms. The GESC method provides more compelling explanations than conventional feature attribution approaches.
Keywords:
Feature attribution
Meta-heuristic optimization algorithm
Explainable artificial intelligence
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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