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  • Notice: Trying to access array offset on value of type int in element_children() (line 6609 of /var/data/sites/methodi.ca-7.63/includes/common.inc).
  • Notice: Trying to access array offset on value of type int in element_children() (line 6609 of /var/data/sites/methodi.ca-7.63/includes/common.inc).
  • Notice: Trying to access array offset on value of type int in element_children() (line 6609 of /var/data/sites/methodi.ca-7.63/includes/common.inc).
  • Notice: Trying to access array offset on value of type int in element_children() (line 6609 of /var/data/sites/methodi.ca-7.63/includes/common.inc).
  • Notice: Trying to access array offset on value of type int in element_children() (line 6609 of /var/data/sites/methodi.ca-7.63/includes/common.inc).
  • Notice: Trying to access array offset on value of type int in element_children() (line 6609 of /var/data/sites/methodi.ca-7.63/includes/common.inc).
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Advanced

Multidimensional Scaling (MDS) is a method to convert sets of document terms into a data frame that can then be visualized. The distances expressed in the visualization show how similar, or dissimilar, the contents of one text are to another. This recipe deals with several advanced text analysis concepts and methods. Links are provided to additional information on these terms.

Panda Data frame: https://pandas.pydata.org/

TF-IDF: https://en.wikipedia.org/wiki/Tf%E2%80%93idf

Principal Component Analysis (PCA) is a method to convert sets of document terms into a data frame that can then be visualized. The distances expressed in the visualization show how similar, or dissimilar, the contents of one text are to another. PCA tries to identify a smaller number of uncorrelated variables, called "principal components" from the dataset. The goal is to explain the maximum amount of variance with the fewest number of principal components.  This recipe deals with several advanced text analysis concepts and methods.

In this recipe we use 3 ebooks to show how topic analysis can identify the different topics each text represents. We will use Latent Dirichlet Allocation (LDA) approach which is the most common modelling method to discover topics. We can then spice it up with an interactive visualization of the discovered themes. This recipe is based on Zhang Jinman's notebook found on TAPoR.

NB: Any number of texts can be used, we choose 3 for this recipe.