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首页> 外文期刊>Journal of supercomputing >Parallelized root cause analysis using cause-related aspect formulation technique (CRAFT)
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Parallelized root cause analysis using cause-related aspect formulation technique (CRAFT)

机译：使用原因相关方面公式化技术（CRAFT）的并行根本原因分析

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摘要

Aspect-based opinion mining aims to provide results that aid in effective business decision making. Identifying the aspects, their major and minor causes proves to be the major challenge in this domain. This paper presents a cause-related aspect formulation technique (CRAFT) to perform opinion mining. The CRAFT model incorporates an enhanced aspect extraction module, ontology creation based on aspect and aspect categories, aspect and aspect category metadata repository creation and maintenance and a decision tree-based parallelized boosted ensemble. The proposed CRAFT model is implemented in Spark to incorporate parallelism in the architecture. The process of ontology creation and metadata repository creation aids in effective identification of both implicit and explicit aspects. Experiments were conducted using a customer review benchmark dataset incorporating reviews about five varied products. Comparisons were performed with state-of-the-art models CNN+LP, Popscu and TF-RBM. Comparisons indicate improved performances ranging up to 4% in terms of precision, up to 18% in terms of recall and up to 11% on F1 Scores, indicating the effectiveness of the proposed CRAFT model.
机译：基于方面的观点挖掘旨在提供有助于有效业务决策的结果。识别方面，其主要和次要原因被证明是该领域的主要挑战。本文提出了一种与因果相关的方面表达技术（CRAFT）来进行观点挖掘。 CRAFT模型包含一个增强的方面提取模块，基于方面和方面类别的本体创建，方面和方面类别元数据存储库的创建和维护以及基于决策树的并行增强集成。提议的CRAFT模型在Spark中实现，以将并行性纳入体系结构中。本体创建和元数据存储库创建的过程有助于有效地识别隐式和显式方面。实验是使用客户评论基准数据集进行的，其中包含了有关五种不同产品的评论。使用最新模型CNN + LP，Popscu和TF-RBM进行比较。比较表明，改进后的性能在精度方面高达4％，在召回方面高达18％，在F1得分上高达11％，表明了所提出的CRAFT模型的有效性。

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