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所在平台: Udemy |
课程主页: https://www.udemy.com/course/natural-language-processing-in-r-for-beginners/
课程评论：没有评论
课程名称：R语言初学者自然语言处理 课程概述：处理文本数据并不复杂！本课程将为初学者讲解复杂主题，帮助大家轻松掌握自然语言处理。完成本课程后，您将能够从Twitter和维基百科等网站读取数据，进行清洗和分析。课程专为熟悉R语言但对自然语言处理或统计学毫无基础的数据分析师设计。课程分为三个主要部分：文本挖掘、准备和探索文本数据以及分析文本数据。 文本挖掘：在进行任何实际工作之前，数据必须存在并处于可用格式。本部分内容包括API使用、Twitter数据获取、网页抓取和维基百科数据处理。 准备和探索文本数据：一旦数据被收集和挖掘，就需要将其转换为可用格式。这部分教程将涵盖如何清洗和探索文本数据，包括正则表达式（Regex）、stringr包、tidytext包和tm包的使用。 分析文本数据：在完成探索性数据分析后，我们可以深入分析文本中的关系和含义。本部分内容包括TF-IDF、情感分析、主题建模、词性标注、命名实体识别和词嵌入。 欢迎加入本课程，发现您文本数据中隐藏的见解！
Working with text data does not need to be difficult!Follow along as we explain complex topics for a beginner audience. By the end of this course, you will be able to read in data from websites like twitter and wikipedia, clean it, and perform analysis.We keep it easy.This course is designed for a data analyst who is familiar with the R language but has absolutely no background in natural language processing or even statistics in general.We break our course into three main sections: text mining, preparing and exploring text data, and analyzing text data.Text MiningLike with every other form of analytics, before any real work can be done, the data must exist (obviously) and be in a working format.What's Covered: APIs, Twitter Data, Webscraping, Wikipedia DataPreparing and Exploring Text DataOnce the data has been properly gathered and mined, it needs to be put into a usable format. The following tutorials cover how to clean and explore text data.What's Covered: Regex, stringr package, tidytext package, tm packageAnalyzing Text DataAfter exploratory data analysis has been performed, we can do further analysis of the relationships and meaning in text.What's Covered: TF-IDF, Sentiment Analysis, Topic Modeling, Parts of Speech Tagging, Name Entity Recognition, Word EmbeddingsSo dive in and see what insights are hiding in your text data!
