title: Text data using logistic regression
body: <p>I am working on a pre-processing phase with this data:</p>
  <pre><code>Name   Nickname   Age    Country    Reg_Date     Text 
  Matt   LeBron    63     Canada     24-12-2008   I'm in a happy mood today. I go to beach
  Chris  Severine  54     U.S.       15-07-2009   I stand in solidarity with #ows
  Lucas  Daly      47     Ireland    01-05-2020   Trump is working for next politician...
  Clash  Lynch     24     U.S.       13-11-2008   What a wonderful day!
  ...
  </code></pre>
  <p>I need is to use Bag of Words or another feature representation before splitting my dataset into training and test set and use it for a logistic regression.</p>
  <p>Currently I tried to get other information from the original dataset above (number of characters in a tweet; use of punctuations, and so on):</p>
  <pre><code>Name   Nickname   Age    Country    Reg_Date     Text 
  Matt   LeBron    63     Canada     24-12-2008   I'm in a happy mood today. I go to beach
  Chris  Severine  54     U.S.       15-07-2009   I stand in solidarity with #ows
  Lucas  Daly      47     Ireland    01-05-2020   Trump is working with Putin... 
  Clash  Lynch     24     U.S.       13-11-2008   What a wonderful day!
  ...
  Lulu   Lulu22    18     Poland     02-09-2019   I hate Maths!!!! &gt;(
  
  
  Punctuation   Positive Words     Negative Words
  [.]          [happy]              []
  [#]          [solidarity]         []
  [...]        []                   []
  [!]          [wonderful]          []
  [&gt;,(]        []                   [hate]
  </code></pre>
  <p>Now, I would really like to understand how I can transform punctuation information, positive words, negative words, text in a way that can be 'readable' for a model (for example in a logistic regression model).</p>
  <p>I would appreciate if you could give me some useful tips or provide an example.</p>