gistfile1.py

from sussex_nltk.parse import dep_parse_sentences_arceager       # Import the function which parses an iterable of sentences
from sussex_nltk.corpus_readers import AmazonReviewCorpusReader  # Import the corpus reader
from nltk import pos_tag                                         # Import the PoS tagging function

# Create a list of reviews that contain the verb "to buy", 
# by filtering for several of its conjugations

sentences = []
verb_variants = set(["buy","buys","bought"])
# You can use any product category (or even all product categories), 
# but DVD is small and takes less time to process
for sentence in AmazonReviewCorpusReader().category("dvd").sents(): 
# Check for several variations of the verb. "&" finds the intersection 
# of 2 sets (what they have in common). So the below statement says 
# "if the set of sentence tokens has any token in common with our verbs, 
# then keep the sentence"
    if verb_variants & set(sentence):
        sentences.append(sentence)  # Populate our list of sentences with 
                                    # this sentence if it contains what 
                                    # we're after.

# Optionally limit the number of reviews to *n* for ease of processing 
# and observation. Or perform some kind of sampling (not shown)
n = 10
sentences = sentences[:n]  # Slice notation

#Create an iterable over dependency parsed sentences
# Round brackets create a generator instead of a list, see the link below 
# the code for an explanation of generator expressions. This means that the 
# sentences will only be PoS-tagged as and when the parser needs them, one 
# at a time.
tagged_sents = (pos_tag(sentence) for sentence in sentences) 
parsed_sents = dep_parse_sentences_arceager(tagged_sents)

# Now you can iterate over *parsed_sents* performing computations or printing
for sentence in parsed_sents:
    print "------ Sentence ------"
    print sentence 
# This sentence is a ParsedSentence object, which is explained further in 
# the next section.