anchor.diff:  pip3 install gensim pandas numpy pyLDAvis
   ----
   
  +*Note:* In case pip install produces an error, try its predecessor __easy_install__. Not recommended on OS X, though, as the command defaults to the 2.7 Python installation that is shipped with OS X.
   *Note:* pyLDAvis is currently not available under Windows (as of
  -10/2015)
  +02/2016)
   
   Also needed for this recipe is the widely used visualization
   package __matplotlib__ for which installation directions are a bit
  
positive.code: 
  * Linux (Ubuntu 14.04, 15.10)
  
  * Windows 7/8
  
  Furthermore, the pipeline depends on a *internet connection* when
  running to download the models for the current configuration. It does
  
negative.code: different languages and different types of text. An independently
  developed NER component trained on German 19th century novels will be
  included in a later version of the pipeline to address use cases like
  this. The following lines will add all named entities to the stopword list.
  
  [source, python]
  ----
  df = df.groupby('NamedEntity')
  
  names = df.get_group('B-PER')['Lemma'].values.astype(str)
  
  names += df.get_group('I-PER')['Lemma'].values.astype(str)
  
  stopwords += names.tolist()
  ----
  
  In the meanwhile, and as a more generic approach, we filter
  out all proper nouns (NP).
  
  [source, python]
  ----
  