def high_words(posids, negids, cutoff, score_fn=BigramAssocMeasures.chi_sq, min_score=5):
word_fd = FreqDist()
label_word_fd = ConditionalFreqDist()
pos = 0
neg = 0
for review in posids:
pos += 1
if (pos != cutoff):
for word in review['text'].split(' '):
word_fd.update(token_helpers.tokenize_simple(word))
label_word_fd['pos'].update(token_helpers.tokenize_simple(word))
for review in negids:
neg += 1
if (neg != cutoff):
for word in review['text'].split(' '):
word_fd.update(token_helpers.tokenize_simple(word))
label_word_fd['neg'].update(token_helpers.tokenize_simple(word))
pos_word_count = label_word_fd['pos'].N()
neg_word_count = label_word_fd['neg'].N()
total_word_count = pos_word_count + neg_word_count
word_scores = {}
for word, freq in word_fd.items():
pos_score = BigramAssocMeasures.chi_sq(label_word_fd['pos'][word], (freq, pos_word_count), total_word_count)
neg_score = BigramAssocMeasures.chi_sq(label_word_fd['neg'][word], (freq, neg_word_count), total_word_count)
word_scores[word] = pos_score + neg_score
best = sorted(word_scores.items(), key=itemgetter(1), reverse=True)[:10000]
bestwords = set([w for w, s in best])
return bestwords
"""
def tokenize_advanced(text, weight):
"""
Tokenizes a string and returns it as a bag of words, including bigrams
Parameters
----------
text : str
A string of raw text to be tokenized
weight : int
The weight to give to each bigram
Returns
-------
list of strs
One string for each token in the document, in the same order as the original
"""
global counter
counter = counter + 1
print(counter)
tokens = token_helpers.tokenize_simple(text)
tagged_tokens = pos_tag(tokens, tagset="universal")
result = []
previous = None
# Will be replaced later with internal functions from a pos.py file
for index, t in enumerate(tagged_tokens):
if t[1] == "ADJ" and index != 0:
previous = tagged_tokens[index - 1]
result.append(previous[0] + " " + t[0])
for bigram in result:
tokens = tokens + ([bigram] * weight)
return tokens
def get_count_vect(data):
"""
Takes json data and return a document term matrix consisting of all the tokens
within the text of review data and how many times they are used (with stopwords
removed)
Parameters
----------
data : list of dicts
The yelp data to be analyzed
Returns
-------
A transformed count vectorizer
Returns a document term matrix of all tokens and their frequencies
"""
tokens = []
count_vect = CountVectorizer(stop_words=stopwords.words("english"))
for review in data:
tokens += token_helpers.tokenize_simple(review["text"])
return count_vect.fit_transform(tokens)
def get_freq_dist(data):
"""
Takes json data and returns its text as a Frequency Distribution
Parameters
----------
data : list of dicts
The yelp data to be analyzed
Returns
-------
An nltk Frequency Distribution
Returns the frequency distribution of all tokens within the reviews
of the yelp data
"""
tokens = []
for review in data:
tokens += token_helpers.tokenize_simple(review["text"])
tokens = token_helpers.remove_stopwords_inner(
tokens,
stopwords=stopwords.words("english")
+ ["time", "would", "got", "i'm", "-", "food", "like", "really", "service"],
)
return FreqDist(tokens)
