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This syntax if word.lower() in self.positives == True: is the problem. Use if word.lower() in self.positives (without the equality). I don't remember the exact evaluation/operator precedence rules tha …

One problem is here self.positives.add(line.strip). line.strip returns the method whereas line.strip() returns the result of the method. (Ditto negatives). You could do a quick test in command-line p …

As per the spec [emphasis added]: ...among whose features is a tokenizer that you can use to split a tweet (which is maximally a 140-character str object) into a list of words (i.e., shorter str o …

Big problem here str(tokensii). tokenizer returns a list. str returns a string. for token in tokens iterates over each character in the string. It may look ok here print(tokens), but tokens is not a l …

poss and negs are not instance variables, so they are not available to analyze. From the Hints section of the spec: If you would like a variable to be accessible from both __init__ and analyze, …

This line token.lower() does not change token to lower case, it returns a copy of token in lower case. You can either set something to that return value (as with word = token.lower()) and then test fo …

One problem in analyze is with split here self.negatives.extend(line.split()). split returns a list. You would have to put token in a list to find it in self.positives or self.negatives. Or perhaps y …

You don't have a "thing" named tokenizer. Review the hints section of the spec: Odds are you’ll find nltk.tokenize.casual.TweetTokenizer of interest, which can be used to tokenize a tweet (i.e., …