After training my own classifier with nltk, how do I load it in textblob?

Question

The built-in classifier in textblob is pretty dumb. It's trained on movie reviews, so I created a huge set of examples in my context (57,000 stories, categorized as positive or negative) and then trained it using nltk. I tried using textblob to train it but it always failed:

with open('train.json', 'r') as fp:
    cl = NaiveBayesClassifier(fp, format="json")

That would run for hours and end in a memory error.

I looked at the source and found it was just using nltk and wrapping that, so I used that instead, and it worked.

The structure for nltk training set needed to be a list of tuples, with the first part was a Counter of words in the text and frequency of appearance. The second part of tuple was 'pos' or 'neg' for sentiment.

>>> train_set = [(Counter(i["text"].split()),i["label"]) for i in data[200:]]
>>> test_set = [(Counter(i["text"].split()),i["label"]) for i in data[:200]] # withholding 200 examples for testing later

>>> cl = nltk.NaiveBayesClassifier.train(train_set) # <-- this is the same thing textblob was using

>>> print("Classifier accuracy percent:",(nltk.classify.accuracy(cl, test_set))*100)
('Classifier accuracy percent:', 66.5)
>>>>cl.show_most_informative_features(75)

Then I pickled it.

with open('storybayes.pickle','wb') as f:
    pickle.dump(cl,f)

Now... I took this pickled file, and re opened it to get the nltk.classifier 'nltk.classify.naivebayes.NaiveBayesClassifier'> -- and tried to feed it into textblob. Instead of

from textblob.classifiers import NaiveBayesClassifier
blob = TextBlob("I love this library", analyzer=NaiveBayesAnalyzer())

I tried:

blob = TextBlob("I love this library", analyzer=myclassifier)
Traceback (most recent call last):
  File "<pyshell#116>", line 1, in <module>
    blob = TextBlob("I love this library", analyzer=cl4)
  File "C:\python\lib\site-packages\textblob\blob.py", line 369, in __init__
    parser, classifier)
  File "C:\python\lib\site-packages\textblob\blob.py", line 323, in 
_initialize_models
    BaseSentimentAnalyzer, BaseBlob.analyzer)
  File "C:\python\lib\site-packages\textblob\blob.py", line 305, in 
_validated_param
    .format(name=name, cls=base_class_name))
ValueError: analyzer must be an instance of BaseSentimentAnalyzer

what now? I looked at the source and both are classes, but not quite exactly the same.

Answer 1

I wasn't able to be certain that a nltk corpus cannot work with textblob, and that would surprise me since textblob imports all of the nltk functions in its source code, and is basically a wrapper.

But what I did conclude after many hours of testing is that nltk offers a better built-in sentiment corpus called "vader" that outperformed all of my trained models.

import nltk
nltk.download('vader_lexicon') # do this once: grab the trained model from the web
from nltk.sentiment.vader import SentimentIntensityAnalyzer
Analyzer = SentimentIntensityAnalyzer()
Analyzer.polarity_scores("I find your lack of faith disturbing.")
{'neg': 0.491, 'neu': 0.263, 'pos': 0.246, 'compound': -0.4215}
CONCLUSION: NEGATIVE

vader_lexicon and nltk code does a lot more parsing of negation language in sentences in order to negate positive Words. Like when Darth Vader says "lack of faith" that changes the sentiment to its opposite.

I explained it here, with examples of the better results: https://chewychunks.wordpress.com/2018/06/19/sentiment-analysis-discovering-the-best-way-to-sort-positive-and-negative-feedback/

That replaces this textblob implementation:

from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer
TextBlob("I find your lack of faith disturbing.", analyzer=NaiveBayesAnalyzer())
{'neg': 0.182, 'pos': 0.817, 'combined': 0.635}
CONCLUSION: POSITIVE

The vader nltk classifier also has additional documentation here on using it for sentiment analysis: http://www.nltk.org/howto/sentiment.html

textBlob always crashed my computer with as little as 5000 examples.