Yelp分析和评级预测 概述 Yelp是一个带有社交网络工具的区域目录平台和审阅网站。 该网站提供了针对本地企业（水疗中心，餐厅，百货公司，酒吧，本地本地服务，商店，汽车）的众包评论。 这有助于用户进行业务评级和评论。 通常，评论是由几百行左右的单词组成的简短文本，描述了各个方面的各种用户体验。 这为企业所有者提供了改进产品的机会，并使客户可以选择最佳的行业。 商业价值/分析目标 管理层可能没有足够的时间来进行每一次审核。 如果可以一目了然地向他们提供有价值的信息和见解，那将是非常有用和节省时间的。 不仅对于管理人员，而且对于试图了解更多餐厅信息并需要一些帮助来订购或选择餐厅的客户，也是如此。 毕竟，在当今世界，每个人都喜欢在做出决定之前先阅读评论和反馈。 在我们的项目中，我们使用自然语言处理和机器学习来实现这些业务和客户目标。 我们专注于情感分析，主题建模，数据分析和评级预测的分类。 数

分布式计算-PySpark 该存储库包含有关在Python中使用Spark进行分布式计算的微型项目。 文本分析：PySpark中的逐点相互信息 计算文本文件中出现的所有单词的一个或多个标记的PMI。 图/网络分析：PySpark中的个性化PageRank算法 实现PageRank算法的修改版本，其中参照给定的源节点执行排名。 修改有两个方面： 随机仅跳到源节点 由于节点悬空而造成的质量损失将完全转移到源节点，而不是在整个图形上重新分配 使用Spark数据帧和Spark SQL查询TPCH

DSO560自然语言处理和文本分析 松弛的工作区 我们将在大多数课程交流中使用Slack工作区，包括提交课堂作业，作业和评分。 您的最终成绩将发布在Blackboard上，但是有关作业的反馈将通过Slack传递。 在第1周的课程之前，请。 请注意，这是与USC Marshall自动创建的工作空间不同的Slack工作空间（我正在创建自己的工作空间，因为我需要额外的权限才能通过Slack chatbot应用自动发送成绩/通知）。 第一周（3月16日，星期二） Google Colab笔记本（请务必保存自己的副本） 第二周 Google Colab笔记本（请务必保存自己的副本） 第三周 第四周 第五周 第六周 第七周 第八周

Paperback: 674 pages Publisher: WOW! eBook; 2nd edition (June 21, 2019) Language: English ISBN-10: 1484243536 ISBN-13: 978-1484243534

Text_Analytics_with_Python.pdf Text_Analytics_with_Python.pdf Text_Analytics_with_Python.pdf

Apress 2019 版 Data is the new oil and unstructured data—especially text, images, and videos—contains a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural language processing (NLP) is all about leveraging tools, techniques, and algorithms to process and understand natural language-based data, which is usually unstructured like text, speech, and so on. In this book, we will be looking at tried and tested strategies—techniques and workflows—that can be leveraged by practitioners and data scientists to extract useful insights from text data.

MATLAB Text Analytics Toolbox官方教程，包含实例和参考手册

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern What you will learn Natural Language concepts Analyzing Text syntax and structure Text Classification Text Clustering and Similarity analysis Text Summarization Semantic and Sentiment analysis Readership The book is for IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from