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Text Mining Approach for Product Quality Enhancement: (Improving Product Quality through Machine Learning)

机译：文本挖掘方法，用于提高产品质量：（通过机器学习提高产品质量）

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Text mining, also referred to as text data mining, is the process of extracting interesting and non-trivial patterns or knowledge from text documents. It uses algorithms to transform free flow text (unstructured) into data that can be analyzed (structured) by applying Statistical, Machine Learning and Natural Language Processing (NLP) techniques. Text mining is an evolving technology that allows enterprises to understand their customers well, and help them in redefining customer needs. As e-commerce is becoming more and more established, the number of customer reviews and feedback that a product receives has grown rapidly over a period of time. For a popular asset, the number of review comments can be in thousands or even more. This makes it difficult for the manufacturer to read all of them to make an informed decision in improving product quality and support. Again it is difficult for the manufacturer to keep track and to manage all customer opinions. This article attempts to derive some meaningful information from asset reviews which will be used in enhancing asset features from engineering point of view and helps in improving the support quality and customer experience.
机译：文本挖掘，也称为文本数据挖掘，是从文本文档中提取有趣且不平凡的模式或知识的过程。它使用算法将自由流文本（非结构化）转换为可以通过应用统计，机器学习和自然语言处理（NLP）技术进行分析（结构化）的数据。文本挖掘是一项不断发展的技术，它使企业能够很好地了解其客户，并帮助他们重新定义客户需求。随着电子商务的日益建立，产品收到的客户评论和反馈的数量在一段时间内迅速增长。对于受欢迎的资产，评论评论的数量可以成千上万甚至更多。这使得制造商难以阅读所有产品，以做出明智的决定来提高产品质量和支持。同样，制造商很难跟踪和管理所有客户意见。本文试图从资产审查中得出一些有意义的信息，这些信息将从工程角度用于增强资产功能，并有助于改善支持质量和客户体验。

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