def tokeniseText(self, doc, isFile, stemFlag):
stemmer = PorterStemmer()
tokens = dict()
stopWords = self.loadStopWords()
fh = list()
if isFile is True:
fh = open(doc)
else:
fh.append(doc)
for line in fh:
line = re.sub('(<.*>)', '', line)
line = re.sub('[^0-9a-zA-Z]+', ' ', line)
line = line.strip().lower()
words = line.split()
if stemFlag is True:
for word in words:
if word not in stopWords:
word = stemmer.stem(word, 0,
len(word) - 1)
if len(word) > 1 and word not in stopWords:
tokens[word] = tokens.get(word, 0) + 1
else:
for word in words:
if len(word) > 1:
tokens[word] = tokens.get(word, 0) + 1
return tokens
def group_stems(total_count, individual_counts, occurence_per_doc):
"""Use the Porter Stemmer algorithm to take only the stems of words and then group them together as a single
count. For instance, run and running might both be in the counts, hence we reduce this to just run."""
stemmer = PorterStemmer()
new_individual_counts = {}
new_total_counts = Counter()
new_occurences_per_doc = Counter()
for file_name, counts in individual_counts.iteritems():
file_counts = Counter()
for word, count in counts.iteritems():
word_stem = stemmer.stem(word, 0, len(word) - 1)
file_counts[word_stem] += count
new_individual_counts[file_name] = file_counts
for word, count in total_count.iteritems():
word_stem = stemmer.stem(word, 0, len(word) - 1)
new_total_counts[word_stem] += count
for word, count in occurence_per_doc.iteritems():
word_stem = stemmer.stem(word, 0, len(word) -1)
new_occurences_per_doc[word_stem] += count
print "Finished grouping words by their stems."
return new_total_counts, new_individual_counts, new_occurences_per_doc
def load_data(stem=True):
ps = PorterStemmer()
positive = []
negative = []
directory = os.fsencode("./NEG")
for file in os.listdir(directory):
filename = os.fsdecode(file)
f = open('./NEG/' + filename, 'r')
text = f.read()
if stem:
text = ps.stem(text, 0, len(text) - 1)
negative.append(text)
directory = os.fsencode("./POS")
for file in os.listdir(directory):
filename = os.fsdecode(file)
f = open('./POS/' + filename, 'r')
text = f.read()
if stem:
text = ps.stem(text, 0, len(text) - 1)
positive.append(text)
target_pos = []
target_neg = []
for i in range(0, 1000):
target_pos.append(0)
for i in range(0, 1000):
target_neg.append(1)
X = positive + negative
y = target_pos + target_neg
return X, y
def __init__(self, stopwords_io_stream = None):
self.stemmer = PorterStemmer()
if(not stopwords_io_stream):
stopwords_io_stream = open(Parser.STOP_WORDS_FILE, 'r')
self.stopwords = stopwords_io_stream.read().split()
def processTweet(tweet):
# Remove HTML special entities (e.g. &)
tweet = re.sub(r'\&\w*;', '', tweet)
#remove @username
tweet = re.sub('@[^\s]+', '', tweet)
# Remove tickers
tweet = re.sub(r'\$\w*', '', tweet)
# To lowercase
tweet = tweet.lower()
# Remove hyperlinks
tweet = re.sub(r'https?:\/\/.*\/\w*', '', tweet)
# Remove hashtags
tweet = re.sub(r'#\w*', '', tweet)
# Remove Punctuation and split 's, 't, 've with a space for filter
tweet = re.sub(r'[' + string.punctuation.replace('@', '') + ']+', ' ',
tweet)
# Remove words with 2 or fewer letters
tweet = re.sub(r'\b\w{1,2}\b', '', tweet)
# Remove whitespace (including new line characters)
tweet = re.sub(r'\s\s+', ' ', tweet)
# Remove single space remaining at the front of the tweet.
tweet = tweet.lstrip(' ')
# Removing Stopwords from tweet using sklearn.feature_extraction
split_list = tweet.split(" ")
tweet = [
word for word in split_list
if word not in stop_words.ENGLISH_STOP_WORDS
]
# Stemming the
ps = PorterStemmer()
tweet = [ps.stem(word) for word in tweet]
tweet = ' '.join(tweet)
return tweet
def stem(words: List[str]):
p = PorterStemmer()
output = []
for word in words:
if word.isalpha():
output.append(p.stem(word, 0,len(word)-1))
return output
def stem():
for line in sys.stdin:
line = line.strip('\n')
if line:
token = line.split('\t')[1]
ps = PorterStemmer().stem(token, 0, len(token) - 1)
print line + '\t' + ps
class Tokenizer(object):
def __init__(self):
self.stemmer = PorterStemmer()
def __call__(self, tweet):
# TODO: This function takes in a single tweet (just the text part)
# then it will process/clean the tweet and return a list of tokens (words).
# For example, if tweet was 'I eat', the function returns ['i', 'eat']
# Lowercase the tweet and strip punctuations
parsed = tweet.lower().replace(',',' ').replace('.',' ').replace('?',' ').replace('!',' ').replace('-',' ').replace('#',' ').replace(':',' ').replace('(',' ').replace(')',' ').replace('=',' ').replace('...',' ').split()
for i, word in enumerate(parsed):
# do replacement
if '@' in word:
parsed[i] = 'AT_USER'
if ('www.' in word) or ('.com' in word) or ('http://' in word) or ('http(s)://' in word):
parsed[i] = 'URL'
# Replacing three or more occurrence of the same character with one occurrence using regular expression
parsed[i] = re.sub(r'(.)\1{2,}', r'\1', parsed[i])
# Apply stemming to each term to restore its original form
res = [self.stemmer.stem(kw, 0, len(kw)-1) for kw in parsed]
# De-duplicate the term list to make it only contains distinct terms
res = list(set(res))
# You will not need to call this function explictly.
# Once you initialize your vectorizer with this tokenizer,
# then 'vectorizer.fit_transform()' will implictly call this function to
# extract features from the training set, which is a list of tweet texts.
# So once you call 'fit_transform()', the '__call__' function will be applied
# on each tweet text in the training set (a list of tweet texts),
return res
def __call__(self, tweet):
# TODO: This function takes in a single tweet (just the text part)
# then it will process/clean the tweet and return a list of tokens (words).
# For example, if tweet was 'I eat', the function returns ['i', 'eat']
# You will not need to call this function explictly.
# Once you initialize your vectorizer with this tokenizer,
# then 'vectorizer.fit_transform()' will implictly call this function to
# extract features from the training set, which is a list of tweet texts.
# So once you call 'fit_transform()', the '__call__' function will be applied
# on each tweet text in the training set (a list of tweet texts),
tweet.lower()
# 1. Lowercase all letters
for i in string.punctuation:
tweet = tweet.replace(i, " ")
words = tweet.split()
result = []
for word in words:
if word[0] == "@": # 7. Removing user references
word = "AT_USER"
if word[0] == "\#": # 5. Removing hashtags
word[0] = word[0].replace("\#", "")
if word[0].isalpha(
): # Ignoring words that don't start with an alphabet letter
if word.startswith("www.") or word.startswith(
"https://") or word.startswith("http://"):
word = "URL"
word = PorterStemmer().stem(word, 0,
len(word) -
1) # 2. Applying stemming
word = re.sub(r'([a-z])\1+', r'\1\1', word)
result.append(word)
return result
class Tokenizer:
def __init__(self, ngrams=1):
self.stemmer = PorterStemmer()
self.ngrams = ngrams
print ' -- initializing tokenizer with maximum ngram = %d' % ngrams
def tokenize(self, tweet):
words = set()
ng = []
# fix the most common HTML escapes
tweet = tweet.replace('"', '').replace(' ',
' ').replace('&', ' ')
# replace certain word-splitting with spaces (e.g. 'foo...bar' should
# be split). do not include '.' because this will incorrectly split URLs
for c in ('...', '..', '&', ','):
tweet = tweet.replace(c, ' ')
for w in tweet.split():
w = w.lower() # convert to lowercase
# get rid of links and user mentions
for s in links:
if s in w: continue
if w.startswith('@'): continue
w = w.strip(punct) # strip punctuation (including hashtag)
if len(w) == 0: continue # ignore now-blank words
if w in stopwords: continue # ignore stopwords
# replace two or more occurrence of same char with two
notrip = ''
for i, x in enumerate(w):
if i < 2 or x != w[i - 2] or x != w[i - 1]: notrip += x
notrip = self.stemmer.stem(
notrip, 0,
len(notrip) - 1) # apply stemming using Porter Stemmer
if not notrip[0].isalpha(): continue
words.add(notrip) # ignore words that don't start with alphabet
if self.ngrams > 1: ng.append(notrip)
# Generate all ngrams and add them
if self.ngrams > 1 and len(words) > 1:
if self.ngrams > len(words): self.ngrams = len(words)
for i in range(len(ng) - self.ngrams + 1):
ngram = ng[i]
for j in range(1, self.ngrams):
ngram += ' ' + ng[i + j]
if ngram == 'madison squar garden': print 'msg'
words.add(ngram)
return words
class Tokenizer:
def __init__(self, ngrams=1):
self.stemmer = PorterStemmer()
self.ngrams = ngrams
print ' -- initializing tokenizer with maximum ngram = %d' % ngrams
def tokenize(self, tweet):
words = set()
ng = []
# fix the most common HTML escapes
tweet = tweet.replace('"','').replace(' ',' ').replace('&',' ')
# replace certain word-splitting with spaces (e.g. 'foo...bar' should
# be split). do not include '.' because this will incorrectly split URLs
for c in ('...','..','&',','):
tweet = tweet.replace(c,' ')
for w in tweet.split():
w = w.lower() # convert to lowercase
# get rid of links and user mentions
for s in links:
if s in w: continue
if w.startswith('@'): continue
w = w.strip(punct) # strip punctuation (including hashtag)
if len(w) == 0: continue # ignore now-blank words
if w in stopwords: continue # ignore stopwords
# replace two or more occurrence of same char with two
notrip = ''
for i,x in enumerate(w):
if i < 2 or x != w[i-2] or x != w[i-1]: notrip += x
notrip = self.stemmer.stem(notrip, 0, len(notrip)-1) # apply stemming using Porter Stemmer
if not notrip[0].isalpha(): continue
words.add(notrip) # ignore words that don't start with alphabet
if self.ngrams>1: ng.append(notrip)
# Generate all ngrams and add them
if self.ngrams>1 and len(words)>1:
if self.ngrams > len(words): self.ngrams = len(words)
for i in range(len(ng)-self.ngrams+1):
ngram = ng[i]
for j in range(1,self.ngrams):
ngram += ' '+ng[i+j]
if ngram == 'madison squar garden': print 'msg'
words.add(ngram)
return words
class Tokenizer(object):
def __init__(self):
self.stemmer = PorterStemmer()
# only admit non-number with length>2
def qualify(self, word):
return len(word)>2 and not word.isdigit()
# TODO: Change the text processing here
# You only need to edit the below function
def process_tweet(self, tweet):
tweet = tweet.lower()
exclude = set(string.punctuation)
tweet = ''.join(ch for ch in tweet if ch not in exclude)
words = tweet.split()
for i in range(len(words)):
words[i] = self.stemmer.stem(words[i], 0, len(words[i]) - 1)
tweet = ' '.join(words)
return tweet
def __call__(self, tweet):
# This function takes in a single tweet (just the text part)
# then it will process/clean the tweet and return a list of tokens (words).
# For example, if tweet was 'I eat', the function returns ['i', 'eat']
# You will not need to call this function explictly.
# Once you initialize your vectorizer with this tokenizer,
# then 'vectorizer.fit_transform()' will implictly call this function to
# extract features from the training set, which is a list of tweet texts.
# So once you call 'fit_transform()', the '__call__' function will be applied
# on each tweet text in the training set (a list of tweet texts),
features = []
for word in self.process_tweet(tweet).split():
if self.qualify(word):
# Stem
word = self.stemmer.stem(word, 0, len(word) - 1)
features.append(word)
return features
class Tokenizer(object):
def __init__(self, stopwords_file):
self.stemmer = PorterStemmer()
self.stop_words = set(line.strip() for line in open(stopwords_file))
def tokenize(self, text):
tokens = []
words = text.lower().split()
for word in words:
word = re.sub(r'[^\w\s]', '', word) # remove punctuation
word = self.stemmer.stem(word, 0, len(word) - 1) # stem
# throw out words in stop words and those starting with non alphabet
if word and word not in self.stop_words and word[0].isalpha():
tokens.append(word)
return tokens
class Tokenizer(object):
def __init__(self, stopwords_file):
self.stemmer = PorterStemmer()
self.stop_words = set(line.strip() for line in open(stopwords_file))
def tokenize(self, text):
tokens = []
words = text.lower().split()
for word in words:
word = re.sub(r'[^\w\s]', '', word) # remove punctuation
word = self.stemmer.stem(word, 0, len(word) - 1) # stem
# throw out words in stop words and those starting with non alphabet
if word and word not in self.stop_words and word[0].isalpha():
tokens.append(word)
return tokens
class Parser:
STOP_WORDS_FILE = '%s/../data/english.stop' % os.path.dirname(
os.path.realpath(__file__))
stemmer = None
stopwords = []
def __init__(self, stopwords_io_stream=None):
self.stemmer = PorterStemmer()
if (not stopwords_io_stream):
stopwords_io_stream = open(Parser.STOP_WORDS_FILE, 'r')
self.stopwords = stopwords_io_stream.read().split()
def tokenise_and_remove_stop_words(self, document_list):
if not document_list:
return []
vocabulary_string = " ".join(document_list)
tokenised_vocabulary_list = self._tokenise(vocabulary_string)
clean_word_list = self._remove_stop_words(tokenised_vocabulary_list)
return clean_word_list
def _remove_stop_words(self, list):
""" Remove common words which have no search value """
return [word for word in list if word not in self.stopwords]
def _tokenise(self, string):
""" break string up into tokens and stem words """
string = self._clean(string)
words = string.split(" ")
return [self.stemmer.stem(word, 0, len(word) - 1) for word in words]
def _clean(self, string):
""" remove any nasty grammar tokens from string """
string = "".join(l for l in string if l not in stg.punctuation)
#string = string.replace("\""," ")
#string = string.replace("\'"," ")
#string = string.replace(":","")
string = string.replace("\s+", " ")
string = string.lower()
return string
class Parser:
STOP_WORDS_FILE = '%s/../data/english.stop' % os.path.dirname(os.path.realpath(__file__))
stemmer = None
stopwords = []
def __init__(self, stopwords_io_stream = None):
self.stemmer = PorterStemmer()
if(not stopwords_io_stream):
stopwords_io_stream = open(Parser.STOP_WORDS_FILE, 'r')
self.stopwords = stopwords_io_stream.read().split()
def tokenise_and_remove_stop_words(self, document_list):
if not document_list:
return []
vocabulary_string = " ".join(document_list)
tokenised_vocabulary_list = self._tokenise(vocabulary_string)
clean_word_list = self._remove_stop_words(tokenised_vocabulary_list)
return clean_word_list
def _remove_stop_words(self, list):
""" Remove common words which have no search value """
return [word for word in list if word not in self.stopwords ]
def _tokenise(self, string):
""" break string up into tokens and stem words """
string = self._clean(string)
words = string.split(" ")
return [self.stemmer.stem(word, 0, len(word)-1) for word in words]
def _clean(self, string):
""" remove any nasty grammar tokens from string """
string = string.replace(".","")
string = string.replace("\s+"," ")
string = string.lower()
return string
class Tokenizer(object):
def __init__(self):
self.stemmer = PorterStemmer()
def process_review(self, tweet):
#TODO: pre-process tweet
# this is a helper function for __call__
tweet = tweet.lower() # To lower case
tweet = re.sub('[^\x20-\x7E]*','',tweet)
tweet = re.sub(r'([a-z0-9])\1+',r'\1\1',tweet) #Replace two or more occurrences of the same character with two occurrences
stopword_file= open("stopwords.txt")
stopwords =set()
stopword_content = stopword_file.readlines()
for word in stopword_content:
stopwords.add(word.strip().lower())
tweet_words = tweet.split()
tweet = ""
for w in tweet_words:
if len(w)< 1 or w in stopwords:
continue
elif not w[0].isalpha():
continue
else:
tweet+= " "+w
tweet = tweet.strip()
tweet = ''.join(ch for ch in tweet if ch not in string.punctuation)
tweet_words = [self.stemmer.stem(word, 0, len(word)-1) for word in tweet.split(" ")]
return tweet_words
def __call__(self, doc):
# this function will tokenize the given document and return a list of extracted features (tokens)
processed_doc = self.process_tweet(doc)
#TODO: return a list of features extracted from processed_doc
return processed_doc
def __init__(self):
self.stemmer = PorterStemmer()
# dic = {"joy": 0, "surprise": 0, "sad": 0, "angry": 0}
def get_text_fromcsv(filename):
with open(filename, "r") as file:
f = csv.reader(file)
d = []
next(file)
for r in f:
l = get_label(r[10:])
if l != "None":
cleaned = ' '.join(clean_words(special_split(r[1])))
# dic[l] = dic[l] + 1
d.append(cleaned + " __label__" + l)
# d.append(cleaned)
return d
delimiters = ['\n', ' ', ',', '.', '?', '!', ':', ';', '#', '$', '[', ']',
'(', ')', '-', '=', '@', '%', '&', '*', '_', '>', '<',
'{', '}', '|', '/', '\\', '\'', '"', '\t', '+', '~',
'^']
stop_words = load_stop_words()
porter = PorterStemmer()
data = get_text_fromcsv("combined.csv")
fi = open("preprocess_combined.txt", "w")
for line in data:
fi.write(line + "\n")
fi.close()
def __init__(self): self.stemmer = PorterStemmer()
from porter_stemmer import PorterStemmer
stemmer = PorterStemmer()
resume = open('assets/resume.txt', 'r').read()
print(stemmer.stem_document(resume))
def __init__(self, ngrams=1): self.stemmer = PorterStemmer() self.ngrams = ngrams print ' -- initializing tokenizer with maximum ngram = %d' % ngrams
def __init__(self, ngrams=1):
self.stemmer = PorterStemmer()
self.ngrams = ngrams
print ' -- initializing tokenizer with maximum ngram = %d' % ngrams
from porter_stemmer import PorterStemmer
stemmer = PorterStemmer()
print stemmer.stem_word("adoption")
print stemmer.stem_word("controll")
print stemmer.stem_word("roll")
print stemmer.stem_word("agreed")
def __init__(self, stopwords_file):
self.stemmer = PorterStemmer()
self.stop_words = set(line.strip() for line in open(stopwords_file))
# custom porter stemmer
from porter_stemmer import PorterStemmer
stemmer = PorterStemmer()
document = "day"
print(stemmer.stem_document(document))
# using the nltk library
# from nltk.stem import PorterStemmer
#
# stemmer = PorterStemmer()
# print(stemmer.stem('day'))
def extract_sentiment_for_movies(self, preprocessed_input):
"""Creative Feature: Extracts the sentiments from a line of
pre-processed text that may contain multiple movies. Note that the
sentiments toward the movies may be different.
You should use the same sentiment values as extract_sentiment, described
above.
Hint: feel free to call previously defined functions to implement this.
Example:
sentiments = chatbot.extract_sentiment_for_text(
chatbot.preprocess(
'I liked both "Titanic (1997)" and "Ex Machina".'))
print(sentiments) // prints [("Titanic (1997)", 1), ("Ex Machina", 1)]
:param preprocessed_input: a user-supplied line of text that has been
pre-processed with preprocess()
:returns: a list of tuples, where the first item in the tuple is a movie
title, and the second is the sentiment in the text toward that movie
"""
#don't need to consider the case where some movies are in the database while the rest are not
title_array = self.extract_titles(preprocessed_input)
if len(title_array) == 1:
return [(title_array[0],
self.extract_sentiment(preprocessed_input))]
stemmer = PorterStemmer()
split_input = preprocessed_input.lower().split()
negate = 1
num_conjunctions = 0
count = 0
in_quotes = False
power = 1
conjunctions = ['and', 'nor', 'but', 'or', 'yet']
neg_list = [
"no", "not", "rather", "couldn't", "wasn't", "didn't", "wouldn't",
"shouldn't", "weren't", "don't", "doesn't", "haven't", "hasn't",
"won't", "wont", "hadn't", "never", "none", "nobody", "nothing",
"neither", "nowhere", "isn't", "can't", "cannot", "mustn't",
"mightn't", "shan't", "without", "needn't"
]
power_list = [
"really", "reeally", "loved", "love", "hate", "hated", "terrible",
"amazing", "fantastic", "incredible", "dreadful", "horrible",
"horrid", "horrendous"
]
sentiment_list = []
for word in split_input:
word = word.strip()
word_no_punc = word.rstrip(",.")
stem = stemmer.stem(word_no_punc, 0, len(word_no_punc) - 1)
if stem.endswith('i'):
stem = stem[:-1] + 'y'
if word.startswith("\""):
in_quotes = True
if word.endswith("\""):
in_quotes = False
continue
if in_quotes:
continue
if word in neg_list and not word.endswith(
","
): # if word in neg_list but ends in comma, negate would be positive
negate = -1 # or have negate * -1
else:
has_comma = False
# maybe include other punctuation?
if word.endswith(","):
has_comma = True
if self.creative:
if word_no_punc in power_list or stem in power_list or word.endswith(
"!"):
power = 2
if word_no_punc in conjunctions or stem in conjunctions:
if (count == 0):
if num_conjunctions != 0:
sentiment_list.append(
sentiment_list[num_conjunctions - 1])
else:
sentiment_list.append(0)
else:
sentiment_list.append(count)
count = 0
num_conjunctions += 1
if word_no_punc in self.sentiment:
if self.sentiment[word_no_punc] == "pos":
count += 1 * negate
else:
count += -1 * negate
elif stem in self.sentiment:
if self.sentiment[stem] == "pos":
count += 1 * negate
else:
count += -1 * negate
if has_comma:
negate = 1
if (count == 0):
sentiment_list.append(sentiment_list[num_conjunctions - 1])
else:
sentiment_list.append(count)
res = []
i = 0
for title in title_array:
curr_count = 0
if sentiment_list[i] > 0:
curr_count = 1 * power
elif sentiment_list[i] < 0:
curr_count = -1 * power
res.append((title, curr_count))
i += 1
return res
class Tokenizer(object):
def __init__(self):
self.stemmer = PorterStemmer()
# only admit non-number with length>2
def qualify(self, word):
return len(word) > 2 and not word.isdigit()
def process_desc(self, desc):
ndesc = []
for word in desc.split():
# lowercase all characters
word = word.lower()
# replace words with hashtags with just the words
if word[0] == "#":
word = word[1:]
# replace words with @ with "AT_USER"
elif word[0] == "@":
word = "AT_USER"
# replace words with url beginnings with "URL"
elif len(word) > 4:
if word[:4] == "www.":
word = "URL"
elif len(word) > 10:
if word[:10] == "http(s)://":
word = "URL"
# strip punctuation using translate string method
translator = str.maketrans('', '', string.punctuation)
word = word.translate(translator)
# use stop words list to filter out low value words
if word not in stop:
# ignore words that are one letter long
if len(word) > 1:
# check to see if the first letter of the word is an alphabetic character
if word[0].isalpha() == True:
# finally check for duplicates
if word not in ndesc:
ndesc.append(word)
return ' '.join(ndesc)
def __call__(self, desc):
# This function takes in a single desc (just the text part)
# then it will process/clean the desc and return a list of tokens (words).
# For example, if desc was 'I eat', the function returns ['i', 'eat']
# You will not need to call this function explictly.
# Once you initialize your vectorizer with this tokenizer,
# then 'vectorizer.fit_transform()' will implictly call this function to
# extract features from the training set, which is a list of desc texts.
# So once you call 'fit_transform()', the '__call__' function will be applied
# on each desc text in the training set (a list of desc texts),
features = []
for word in self.process_desc(desc).split():
if self.qualify(word):
# Stem
word = self.stemmer.stem(word, 0, len(word) - 1)
features.append(word)
return features
def tokenise(self, string):
""" break string up into tokens and stem words """
stemmer = PorterStemmer()
string = self.clean(string)
words = string.split(" ")
return [stemmer.stem(word, 0, len(word) - 1) for word in words]
def extract_sentiment(self, preprocessed_input):
"""Extract a sentiment rating from a line of pre-processed text.
You should return -1 if the sentiment of the text is negative, 0 if the
sentiment of the text is neutral (no sentiment detected), or +1 if the
sentiment of the text is positive.
As an optional creative extension, return -2 if the sentiment of the
text is super negative and +2 if the sentiment of the text is super
positive.
Example:
sentiment = chatbot.extract_sentiment(chatbot.preprocess(
'I liked "The Titanic"'))
print(sentiment) // prints 1
:param preprocessed_input: a user-supplied line of text that has been
pre-processed with preprocess()
:returns: a numerical value for the sentiment of the text
"""
stemmer = PorterStemmer()
split_input = preprocessed_input.lower().split()
negate = 1
count = 0
in_quotes = False
power = 1
neg_list = [
"no", "not", "rather", "couldn't", "wasn't", "didn't", "wouldn't",
"shouldn't", "weren't", "don't", "doesn't", "haven't", "hasn't",
"won't", "wont", "hadn't", "never", "none", "nobody", "nothing",
"neither", "nor", "nowhere", "isn't", "can't", "cannot", "mustn't",
"mightn't", "shan't", "without", "needn't"
]
power_list = [
"really", "reeally", "loved", "love", "hate", "hated", "terrible",
"amazing", "fantastic", "incredible", "dreadful", "horrible",
"horrid", "horrendous"
]
for word in split_input:
word = word.strip()
word_no_punc = word.rstrip(",.")
stem = stemmer.stem(word_no_punc, 0, len(word_no_punc) - 1)
if stem.endswith('i'):
stem = stem[:-1] + 'y'
if word.startswith("\""):
in_quotes = True
if word.endswith("\""):
in_quotes = False
continue
if in_quotes:
continue
if word in neg_list and not word.endswith(
","
): # if word in neg_list but ends in comma, negate would be positive
negate = -1 # or have negate * -1
else:
has_comma = False
# maybe include other punctuation?
if word.endswith(","):
has_comma = True
if self.creative:
if word_no_punc in power_list or stem in power_list or word.endswith(
"!"):
power = 2
if word_no_punc in self.sentiment:
if self.sentiment[word_no_punc] == "pos":
count += 1 * negate
else:
count += -1 * negate
elif stem in self.sentiment:
if self.sentiment[stem] == "pos":
count += 1 * negate
else:
count += -1 * negate
if has_comma:
negate = 1
if count > 0:
return 1 * power
elif count < 0:
return -1 * power
return 0
def __init__(self, stopwords_file):
self.stemmer = PorterStemmer()
self.stop_words = set(line.strip() for line in open(stopwords_file))