Difference between packaged sentiment analysis tools (TextBlob/NLTK) and training your own classifier? I'm new to ML and training classifiers in practice, so I was just wondering what the difference was between the built-in sentiment tools of packages such as NLTK and TextBlob as compared to manually creating a classifier (training, testing, etc). I think I read in a comment somewhere that Textblob/NLTK's existing sentiment analysis tools basically just tokenize the text and count the number of positive/negative words to determine an overall sentiment rating (not sure how accurate this is). Does anyone know if using a custom classifier would, in general, be a better way to doing sentiment analysis of text (I'm looking at analyzing the sentiments expressed in hotel reviews)?
In general, training your own classifier is likely to perform better but it's going to take more time and effort:


Probably the NLTK system was trained on some generic data which might be very different from your target text. Since any supervised system assumes the same distribution between the training and test set, it's always better to train on a sample of your target data. 
By implementing your own method you can adapt it more specifically to your use case.


So it's a trade-off between the level of quality and the time you're ready to spend on annotating your own data and tuning your own classifier.