Introduction to Topic Modelling in R

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Coursera Project Network
在此指導項目中,您將:

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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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.

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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.

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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常見問題

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