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ReviewModus: Text classification and sentiment prediction of unstructured reviews using a hybrid combination of machine learning and evaluation models

机译：点评译文：使用机器学习和评估模型的混合组合，文本分类和情绪预测非结构化审查

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摘要

While research interest on product and service evaluation from unstructured text reviews is increasing, investigating the effectiveness of predictive analytical models in this context is still under-explored. With the advancement in machine learning research, an opportunity exists to bridge this gap using a model-based product and service evaluation. We propose in this article ReviewModus, a text mining and processing framework that (1) relies on the model structure and its corresponding assessment questions to train a machine learning algorithm to predict the classification of reviews around the model dimensions; (2) predicts the sentiments within the reviews based on external review training datasets; and (3) transforms the extracted measures from the reviews for further analysis. Our approach is evaluated in the context of 11 e-government services where the performance of the framework is compared to the manual processing of unstructured reviews crosschecked by three independent evaluators. Our study shows promising classification results with a micro-average F-score reaching 85.16%, and a high sentiment prediction correlation (71.44%) with the manually performed sentiment assessment. (C) 2019 Elsevier Inc. All rights reserved.
机译：虽然研究来自非结构化文本的产品和服务评估的研究兴趣正在增加，但仍然探讨了这种背景下的预测分析模型的有效性。随着机器学习研究的进步，存在使用基于模型的产品和服务评估来弥合这一差距的机会。我们在本文中提出了审查案件，（1）依赖于模型结构及其相应的评估问题，以培训机器学习算法预测模型尺寸的评论的分类; （2）根据外部审查培训数据集预测评论中的情绪; （3）将提取的措施从评论中转变为进一步分析。我们的方法是在11个电子政务服务的背景下进行评估，其中框架的表现与由三名独立评估人员交叉检查的非结构化评论的手动处理进行了比较。我们的研究表明，具有手动表现情绪评估的微平均f分，微平均F分和高情感预测相关性（71.44％）的高度普通F分数。 （c）2019 Elsevier Inc.保留所有权利。

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