Demo None Type not Iterable

Error Output of Code:
----------------------------------------------------
 - Analyze typical example cases, including handling of:
  -- negations
  -- punctuation emphasis & punctuation flooding
  -- word-shape as emphasis (capitalization difference)
  -- degree modifiers (intensifiers such as 'very' and dampeners such as 'kind of')
  -- slang words as modifiers such as 'uber' or 'friggin' or 'kinda'
  -- contrastive conjunction 'but' indicating a shift in sentiment; sentiment of later text is dominant
  -- use of contractions as negations
  -- sentiment laden emoticons such as :) and :D
  -- utf-8 encoded emojis such as 💘 and 💋 and 😁
  -- sentiment laden slang words (e.g., 'sux')
  -- sentiment laden initialisms and acronyms (for example: 'lol') 

VADER is smart, handsome, and funny.----------------------------- {'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8316}
VADER is smart, handsome, and funny!----------------------------- {'neg': 0.0, 'neu': 0.248, 'pos': 0.752, 'compound': 0.8439}
VADER is very smart, handsome, and funny.------------------------ {'neg': 0.0, 'neu': 0.299, 'pos': 0.701, 'compound': 0.8545}
VADER is VERY SMART, handsome, and FUNNY.------------------------ {'neg': 0.0, 'neu': 0.246, 'pos': 0.754, 'compound': 0.9227}
VADER is VERY SMART, handsome, and FUNNY!!!---------------------- {'neg': 0.0, 'neu': 0.233, 'pos': 0.767, 'compound': 0.9342}
VADER is VERY SMART, uber handsome, and FRIGGIN FUNNY!!!--------- {'neg': 0.0, 'neu': 0.294, 'pos': 0.706, 'compound': 0.9469}
VADER is not smart, handsome, nor funny.------------------------- {'neg': 0.646, 'neu': 0.354, 'pos': 0.0, 'compound': -0.7424}
The book was good.----------------------------------------------- {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}
At least it isn't a horrible book.------------------------------- {'neg': 0.0, 'neu': 0.678, 'pos': 0.322, 'compound': 0.431}
The book was only kind of good.---------------------------------- {'neg': 0.0, 'neu': 0.697, 'pos': 0.303, 'compound': 0.3832}
The plot was good, but the characters are uncompelling and the dialog is not great. {'neg': 0.327, 'neu': 0.579, 'pos': 0.094, 'compound': -0.7042}
Today SUX!------------------------------------------------------- {'neg': 0.779, 'neu': 0.221, 'pos': 0.0, 'compound': -0.5461}
Today only kinda sux! But I'll get by, lol----------------------- {'neg': 0.127, 'neu': 0.556, 'pos': 0.317, 'compound': 0.5249}
Make sure you :) or :D today!------------------------------------ {'neg': 0.0, 'neu': 0.294, 'pos': 0.706, 'compound': 0.8633}
Catch utf-8 emoji such as 💘 and 💋 and 😁-------------------------- {'neg': 0.0, 'neu': 0.583, 'pos': 0.417, 'compound': 0.875}
Not bad at all--------------------------------------------------- {'neg': 0.0, 'neu': 0.513, 'pos': 0.487, 'compound': 0.431}
----------------------------------------------------
 - About the scoring: 
  -- The 'compound' score is computed by summing the valence scores of each word in the lexicon, adjusted
     according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive).
     This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence.
     Calling it a 'normalized, weighted composite score' is accurate.
  -- The 'pos', 'neu', and 'neg' scores are ratios for proportions of text that fall in each category (so these
     should all add up to be 1... or close to it with float operation).  These are the most useful metrics if
     you want multidimensional measures of sentiment for a given sentence.
----------------------------------------------------
----------------------------------------------------
 - Analyze examples of tricky sentences that cause trouble to other sentiment analysis tools.
  -- special case idioms - e.g., 'never good' vs 'never this good', or 'bad' vs 'bad ass'.
  -- special uses of 'least' as negation versus comparison 

Sentiment analysis has never been good.------------------------------ {'neg': 0.325, 'neu': 0.675, 'pos': 0.0, 'compound': -0.3412}
Sentiment analysis has never been this good!------------------------- {'neg': 0.0, 'neu': 0.621, 'pos': 0.379, 'compound': 0.5672}
Most automated sentiment analysis tools are shit.-------------------- {'neg': 0.375, 'neu': 0.625, 'pos': 0.0, 'compound': -0.5574}
With VADER, sentiment analysis is the shit!-------------------------- {'neg': 0.0, 'neu': 0.583, 'pos': 0.417, 'compound': 0.6476}
Other sentiment analysis tools can be quite bad.--------------------- {'neg': 0.351, 'neu': 0.649, 'pos': 0.0, 'compound': -0.5849}
On the other hand, VADER is quite bad ass---------------------------- {'neg': 0.0, 'neu': 0.423, 'pos': 0.577, 'compound': 0.802}
VADER is such a badass!---------------------------------------------- {'neg': 0.0, 'neu': 0.598, 'pos': 0.402, 'compound': 0.4003}
Without a doubt, excellent idea.------------------------------------- {'neg': 0.0, 'neu': 0.341, 'pos': 0.659, 'compound': 0.7013}
Roger Dodger is one of the most compelling variations on this theme.- {'neg': 0.0, 'neu': 0.834, 'pos': 0.166, 'compound': 0.2944}
Roger Dodger is at least compelling as a variation on the theme.----- {'neg': 0.0, 'neu': 0.853, 'pos': 0.147, 'compound': 0.2263}
Roger Dodger is one of the least compelling variations on this theme. {'neg': 0.132, 'neu': 0.868, 'pos': 0.0, 'compound': -0.1695}
Not such a badass after all.----------------------------------------- {'neg': 0.289, 'neu': 0.711, 'pos': 0.0, 'compound': -0.2584}
Without a doubt, an excellent idea.---------------------------------- {'neg': 0.0, 'neu': 0.408, 'pos': 0.592, 'compound': 0.7013}
----------------------------------------------------
----------------------------------------------------
 - VADER works best when analysis is done at the sentence level (but it can work on single words or entire novels).
  -- For example, given the following paragraph text from a hypothetical movie review:
    'It was one of the worst movies I've seen, despite good reviews. Unbelievably bad acting!! Poor direction. VERY poor production. The movie was bad. Very bad movie. VERY BAD movie!'
  -- You could use NLTK to break the paragraph into sentence tokens for VADER, then average the results for the paragraph like this: 

It was one of the worst movies I've seen, despite good reviews.------ -0.7584
Unbelievably bad acting!!-------------------------------------------- -0.6572
Poor direction.------------------------------------------------------ -0.4767
VERY poor production.------------------------------------------------ -0.6281
The movie was bad.--------------------------------------------------- -0.5423
Very bad movie.------------------------------------------------------ -0.5849
VERY BAD movie!------------------------------------------------------ -0.7616
AVERAGE SENTIMENT FOR PARAGRAPH:    -0.6299
----------------------------------------------------
----------------------------------------------------
 - Analyze sentiment of IMAGES/VIDEO data based on annotation 'tags' or image labels. 

balloons------- 0.0
cake----------- 0.0
candles-------- 0.0
happy birthday- 0.5719
friends-------- 0.4767
laughing------- 0.4939
smiling-------- 0.4588
party---------- 0.4019
AVERAGE SENTIMENT OF TAGS/LABELS:   0.3004

riot----------- -0.5574
fire----------- -0.34
fight---------- -0.3818
blood---------- 0.0
mob------------ 0.0
war------------ -0.5994
police--------- 0.0
tear gas------- 0.0
AVERAGE SENTIMENT OF TAGS/LABELS:   -0.2348
----------------------------------------------------

Would you like to run VADER demo examples with NON-ENGLISH text? 
 (Note: requires Internet access and uses the 'requests' library) 
 Type 'y' or 'n', then press Enter: y

----------------------------------------------------
 - Analyze sentiment of NON ENGLISH text...for example:
  -- French, German, Spanish, Italian, Russian, Japanese, Arabic, Chinese(Simplified) , Chinese(Traditional)
  -- many other languages supported. 

- English : I'm surprised to see just how amazingly helpful VADER is!                0.6468 (No translation needed)
- French  : Je suis surpris de voir comment VADER est incroyablement utile !         0.6581 (MemoryNet Translation Service)
- German  : Ich bin überrascht zu sehen, nur wie erstaunlich nützlich VADER!         0.6581 (MemoryNet Translation Service)
- Spanish : Me sorprende ver sólo cómo increíblemente útil VADER!                    0.6581 (MemoryNet Translation Service)
- Italian : Sono sorpreso di vedere solo come incredibilmente utile VADER è!         0.6581 (MemoryNet Translation Service)
- Russian : Я удивлен увидеть, как раз как удивительно полезно ВЕЙДЕРА!              0.6581 (MemoryNet Translation Service)
- Japanese: 私はちょうどどのように驚くほど役に立つベイダーを見て驚いています!                                        0.7639 (MemoryNet Translation Service)
- Arabic  : أنا مندهش لرؤية فقط كيف مثير للدهشة فيدر فائدة!                          0.6673 (MemoryNet Translation Service)
Traceback (most recent call last):
  File "C:\Users\Janik\Programming\python_Projekte\plots_daten_auswertung\VADER\ownVader\vaderSentiment\vaderSentiment.py", line 683, in <module>
    vs = analyzer.polarity_scores(translation)
  File "C:\Users\Janik\Programming\python_Projekte\plots_daten_auswertung\VADER\ownVader\vaderSentiment\vaderSentiment.py", line 242, in polarity_scores
    for chr in text:
TypeError: 'NoneType' object is not iterable

Process finished with exit code 1
Happend using fresh installation of NLTK and other modules and on Python 3.9.
cjhutto / vaderSentiment

Demo None Type not Iterable #135
