Text Mining in Practice with R

出版者:Wiley

作者:Kwartler, Ted

页数:320

出版时间:2017-7-24

价格:USD 78.26

装帧:Hardcover

isbn号码:9781119282013

具体描述

Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R.

Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You’ll learn how to:

Identify actionable social media posts to improve customer service Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more

Most companies’ data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents—until now.

作者简介

From the Back Cover

A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You'll learn how to: Identify actionable social media posts to improve customer service Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies' data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents—until now.

Read more

About the Author

TED KWARTLER is a data science instructor at DataCamp.com. He has worked in analytical and executive roles at DataRobot, Liberty Mutual Insurance and Amazon.com.

Read more

用户评价

我必须强调这本书在案例选择上的独到眼光和前瞻性。它所选取的实践案例并非那种陈旧的、仅限于理论讲解的“泰坦尼克号”或“鸢尾花”数据集的变体，而是紧密贴合当前行业热点和实际商业需求的场景。我记得其中一个关于情感倾向分析的案例，作者不仅展示了如何构建一个基础的分类器，更进一步引入了时序分析的概念，用于追踪公众对某个产品在不同时间点反馈的变化趋势，这让我看到了文本挖掘在市场情报领域的巨大潜力。另一个关于主题建模的章节，其复杂性处理得非常巧妙，它没有停留在基础的LDA模型上，而是引入了更具可解释性的非负矩阵分解（NMF）方法，并展示了如何利用外部知识库来提升主题识别的精度。这种紧跟前沿且注重实效性的内容设置，极大地激发了我将所学知识应用于我目前工作中的热情。对于希望快速将理论知识转化为生产力的人来说，这种实践导向的内容是无价的，它弥补了许多学术教材过于理论化、脱离实际的遗憾。

这本书的装帧设计和排版布局着实令人眼前一亮，那种沉稳而不失现代感的封面处理，一下子就抓住了我的注意力。内页的纸张质感也十分考究，阅读起来眼睛不容易疲劳，这对于长时间沉浸在复杂代码和理论分析中的学习者来说，简直是一个福音。我尤其欣赏作者在章节划分上的用心，逻辑过渡自然流畅，仿佛带领读者进行了一场精心策划的探索之旅，从基础概念的铺陈，到高级算法的深入剖析，每一步都踩在了预期的节奏上。书中的插图和图表制作得极为精良，不仅仅是简单的示意，更是对抽象概念的具象化呈现，很多我之前在其他资料上晦涩难懂的地方，通过这些视觉辅助一下子就豁然开朗了。比如，某个复杂的自然语言处理流程图，如果只是文字描述，很容易让人迷失方向，但在这本书里，它被拆解成了几个清晰的模块，配色和箭头指向都恰到好处，极大地提升了学习效率。此外，书本的整体重量和尺寸也拿捏得很好，便于携带和翻阅，即便是带着它去咖啡馆或者图书馆，也不会成为负担。这种对阅读体验的重视，反映出作者对读者群体的深度理解和尊重，细节之处见真章，这本书在物理层面上就已经为一次愉快的学习体验奠定了坚实的基础。

这本书的叙述风格极其平易近人，完全没有那种高高在上的学术腔调，读起来感觉就像是身边一位经验丰富的同事在耐心地为你传授实战技巧。作者似乎深谙“授人以渔”的道理，他并非简单地罗列代码片段，而是深入地解释了每一个决策背后的“为什么”。例如，在讨论特定文本清洗策略时，他会先分析不同类型噪声数据对下游模型性能的潜在负面影响，然后再引出为什么选择特定的正则表达式或分词器更为合适。这种“理论先行，实践支撑”的论述结构，极大地增强了读者的批判性思维能力，让我不再是盲目地复制粘贴代码，而是开始思考在面对新的、未见过的数据集时，应该如何灵活调整策略。书中穿插的那些小型的“陷阱警示”或“最佳实践”提示框，更是点睛之笔，它们往往精准地指出了初学者最容易犯的错误，避免了我在实践中走不必要的弯路。我甚至觉得，这本书不仅仅是在教我如何使用工具，更是在培养我作为一名数据科学家的思维习惯，关注数据质量、评估指标的合理性，以及模型的可解释性，这些软技能的提升，价值远超代码本身。

如果说有什么让我感到惊喜的地方，那就是本书在数据伦理和模型偏差讨论上的深度和坦诚。在当前AI领域对公平性和透明度日益关注的大背景下，很多技术书籍往往选择避重就轻，只关注技术实现。然而，这本书勇敢地开辟了一个专门的章节，深入探讨了在进行文本挖掘和情感分析时，如何识别和缓解数据中潜在的种族、性别或社会偏见。作者不仅提出了理论上的框架，还提供了一些具体的工具和方法论，教导我们如何使用统计检验和可视化手段来诊断模型是否存在歧视性输出。这种对社会责任感的强调，将这本书从一本纯粹的技术手册提升到了一个更具人文关怀的知识载体的层面。它提醒每一位读者，我们手中的技术是强大的，但也必须带着审慎的态度去使用。这种对技术局限性和社会影响的深刻反思，是任何一个严肃的从业者都应该具备的素养，这本书在这方面给予了非常及时的指导和警示，让我受益匪浅。

关于代码的实现和易用性方面，这本书做得可以说是教科书级别的典范。所有的代码示例都经过了严谨的测试和优化，结构清晰，注释详尽且具有高度的自解释性。更令人称道的是，作者似乎非常体贴地考虑到了不同操作系统和R环境下的兼容性问题。比如，在处理某些需要特定外部库依赖的复杂操作时，作者会清晰地指明依赖关系，并提供针对不同配置环境的安装和配置指南，这极大地减少了读者在环境搭建上浪费的时间和精力。我特别欣赏的是，书中提供的所有代码资源都可以通过一个维护良好的在线仓库获取，并且作者似乎还在持续更新和完善这些资源，这使得这本书的生命周期得到了极大的延长。这种对代码质量和可维护性的承诺，对于依赖书本进行长期学习和项目开发的读者来说，是至关重要的信心保障。我发现，很多其他书籍的代码常常在新的包版本发布后就无法运行，但这本书的代码结构似乎更具弹性，体现了作者对稳健编程的深刻理解。

5星给data camp的课程，小哥弄得很好，这本书是和data camp两个课程配套的，一个用bag of words， 另一个是sentiment analysis

5星给data camp的课程，小哥弄得很好，这本书是和data camp两个课程配套的，一个用bag of words， 另一个是sentiment analysis

5星给data camp的课程，小哥弄得很好，这本书是和data camp两个课程配套的，一个用bag of words， 另一个是sentiment analysis

5星给data camp的课程，小哥弄得很好，这本书是和data camp两个课程配套的，一个用bag of words， 另一个是sentiment analysis

5星给data camp的课程，小哥弄得很好，这本书是和data camp两个课程配套的，一个用bag of words， 另一个是sentiment analysis