Introduction to Topic Modelling in R

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Coursera Project Network
在此指导项目中,您将:

Load textual data into R, and pre-process it

Convert textual data into a document feature matrix Run an LDA topic model on your data

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Beginner初级
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By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document.

您要培养的技能

  • sampling
  • Topic Modelling
  • Unsupervised Learning
  • Data Visualization (DataViz)
  • Text Corpus

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Load textual data into R, and pre-process it to prepare it for topic modelling

  2. Convert textual data into a document feature matrix and reduce its dimensionality before applying the model.

  3. Run an LDA topic model on your data and explore the topics identified by the model as well as the most frequently used words associated with each topic.

  4. Plot the change in topics over time in your data as well as to explore the distribution of topic probabilities in each of your textual documents.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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