Coursera上的视频做笔记学习

前言:
其实,semantic network与text mining是紧密相关的。
Semantic network
Text Mining and Analytics(1)

Text Mining and Analytics(1)

Text Mining and Analytics(1)

Text Mining and Analytics(1)
1、so this means the data mining problem is basically taking a lot of data as input and giving actionale knowledge as output
2、在data mining 内部,又有针对不同data类型的mining

Text Mining and Analytics(1)
The main goal of test mining is actually to revert this process of generating text data

Text Mining and Analytics(1)
the context can provide intersting angles for analyzing text data
For example, we might partion text data into different time periods
because of the availability of the time.Now we can analyze text data in each time period and then make a comparison.
Similarly we can partion text data based on locations or any meta data that’s associated to form interesting comparisons in areas.
So in this sense, non-text data can actually provide interesting angles or perspectives for text data analysis.And it can help us make context-sensitive
analysis of content or the language usage or the opinions about the observer or the authors of text data.We could analyze the sentiment in different contexts

Text Mining and Analytics(1)

Text Mining and Analytics(1)

Text Mining and Analytics(1)

Text Mining and Analytics(1)

Text Mining and Analytics(1)

相关文章:

  • 2021-07-02
  • 2022-01-21
  • 2021-08-13
  • 2021-04-19
  • 2021-08-15
  • 2021-05-25
  • 2021-05-02
猜你喜欢
  • 2021-05-28
  • 2021-08-31
  • 2021-06-27
  • 2021-09-13
  • 2021-10-01
  • 2021-07-25
  • 2021-11-21
相关资源
相似解决方案