def buildNGramDict(taggedReviews,
n=1,
applyFn=sentenceSumSentiment,
filterFn=filtering.chainFilter(filtering.lower,
filtering.removeStopwords)):
ngramDict = defaultdict(lambda: 0)
for taggedReview in taggedReviews:
for taggedSentence in taggedReview:
sentenceSentiment = applyFn(taggedSentence)
for ngram in nltk.ngrams(filterFn(filtering.tokenize(taggedSentence.sentence)), n):
ngramDict[ngram] += ngramDict[ngram]
return normalize(ngramDict)
def buildWordSentimentDict(taggedReviews,
applyFn=sentenceSumSentiment,
filterFn=filtering.chainFilter(filtering.lower,
filtering.removeStopwords)):
"""
Builds a dictionary of word sentiments from training data by taking the
running average of applying fn (defaults to sentenceSumSentiment). Filters
out words contained in the filterDict argument.
"""
sentimentDict = defaultdict(lambda: 0)
for taggedReview in taggedReviews:
for taggedSentence in taggedReview:
tokenizedSentence = filtering.tokenize(taggedSentence.sentence)
filteredSentence = filterFn(tokenizedSentence)
for word in filteredSentence:
sentimentDict[word] += applyFn(taggedSentence)
return normalize(sentimentDict)
