def lightStemAr(word_list): result = [] arstemmer = ISRIStemmer() for word in word_list: word = arstemmer.norm(word, num=1) # remove diacritics which representing Arabic short vowels if not word in arstemmer.stop_words: # exclude stop words from being processed word = arstemmer.pre32(word) # remove length three and length two prefixes in this order word = arstemmer.suf32(word) # remove length three and length two suffixes in this order word = arstemmer.waw(word) # remove connective ‘و’ if it precedes a word beginning with ‘و’ word = arstemmer.norm(word, num=2) # normalize initial hamza to bare alif result.append(word) return ' '.join(result)
def lightStemAr(word_list): result = [] arstemmer = ISRIStemmer() for word in word_list: word = arstemmer.norm(word, num=1) # remove diacritics which representing Arabic short vowels if not word in arstemmer.stop_words: # exclude stop words from being processed word = arstemmer.pre32(word) # remove length three and length two prefixes in this order word = arstemmer.suf32(word) # remove length three and length two suffixes in this order word = arstemmer.waw(word) # remove connective ‘و’ if it precedes a word beginning with ‘و’ word = arstemmer.norm(word, num=2) # normalize initial hamza to bare alif result.append(word) return ' '.join(result)
def light_stem(text):
words = text
result = list()
stemmer = ISRIStemmer()
for word in words:
word = stemmer.norm(word, num=1)
if word not in stemmer.stop_words:
word = stemmer.pre32(word)
word = stemmer.suf32(word)
word = stemmer.waw(word)
word = stemmer.norm(word, num=2)
result.append(word)
return ' '.join(result)
def read_data_from_file(
filename, number_of_classes
): #"reading and preparing the training or testing datasets by extracting and separating tweets from labels."
tweets = [] # list of text samples
labels = [] # list of label ids
istemmer = ISRIStemmer()
read_file = open(filename, "r+") # read and write mode
for line in read_file:
tweet = ""
filtered_line = line.split() # to get the tweet itself
label = list(map(int, filtered_line[-11:]))
for word in filtered_line[1:-11]:
tweet += word + " "
tweet = tweet[:-1]
tweet = clean_str(tweet)
tweet = istemmer.norm(tweet)
tweets.append(tweet)
labels.append(label)
read_file.close()
return [tweets, labels]
def read_data_from_file(
filename
): #"reading and preparing the training or testing datasets by extracting and separating tweets from labels."
tweets = [] # list of text samples
labels = [] # list of label ids
labels_index = {} # dictionary mapping label name to numeric id
istemmer = ISRIStemmer()
read_file = open(filename, "r+") # read and write mode
index = 0
for line in read_file:
line = line.split('\t') # to get the tweet itself
label = line[0]
tweet = line[1].strip(" \"")
tweet = clean_str(tweet)
tweet = istemmer.norm(tweet)
if (label not in labels_index):
labels_index[label] = index
index += 1
tweets.append(tweet)
labels.append(labels_index[label])
read_file.close()
return [tweets, labels]
def arabic_social_media_text_filter(txt, debug=0):
"""
This filter is for filtering Arabic text from social media.
:param txt: utf-8 text, unicode
:param debug: Any value greater than 0 prints messages about normalized vs original text.
:param return:
"""
txt = social_media_text_filter(txt, debug=debug)
# Remove diacritics
st = ISRIStemmer()
txt = st.norm(txt)
return txt
def arabic_social_media_text_filter(txt, debug=0):
"""
This filter is for filtering Arabic text from social media.
:param txt: utf-8 text, unicode
:param debug: Any value greater than 0 prints messages about normalized vs original text.
:param return:
"""
txt = social_media_text_filter(txt, debug=debug)
# Remove diacritics
st = ISRIStemmer()
txt = st.norm(txt)
return txt
def light_stem(text):
words = text.split()
result = list()
stemmer = ISRIStemmer()
for word in words:
word = stemmer.norm(word, num=1) # remove diacritics which representing Arabic short vowels
if not word in stemmer.stop_words: # exclude stop words from being processed
word = stemmer.pre32(word) # remove length three and length two prefixes in this order
word = stemmer.suf32(word) # remove length three and length two suffixes in this order
word = stemmer.waw(word) # remove connective ‘و’ if it precedes a word beginning with ‘و’
word = stemmer.norm(word, num=2) # normalize initial hamza to bare alif
# word=stemmer.pro_w4(word) #process length four patterns and extract length three roots
# word=stemmer.pro_w53(word) #process length five patterns and extract length three roots
# word=stemmer.pro_w54(word) #process length five patterns and extract length four roots
# word=stemmer.end_w5(word) #ending step (word of length five)
# word=stemmer.pro_w6(word) #process length six patterns and extract length three roots
# word=stemmer.pro_w64(word) #process length six patterns and extract length four roots
# word=stemmer.end_w6(word) #ending step (word of length six)
# word=stemmer.suf1(word) #normalize short sufix
# word=stemmer.pre1(word) #normalize short prefix
result.append(word)
return ' '.join(result)
class BasicStemmer(Stemmer):
def __init__(self):
self.stemmer = ISRIStemmer()
self.stopWordsIndex = ArabicStopWordsIndex(self)
self.stopWordsIndex.buildIndex()
def getStems(self, tokens, flag=False):
rootList = []
for token in tokens:
#token=stemmer.norm(token)
root = self.stemmer.pre32(token)
rootList.append(root)
print(token, " : ", root)
return rootList
def stem(self, word):
root = self.stemmer.pre32(word)
root = self.stemmer.norm(root, 3)
return root
def loadStemsDictionnary(self, filePath="dictStems.txt"):
lines = open(filePath, "r", encoding="windows-1256").readlines()
dictionary = nltk.defaultdict(list)
for line in lines:
if not re.match("^;.*", line):
parts = line.split('\t')
if len(parts) != 4:
break
else:
[rootStem, stem, tag, enGloss] = parts
dictionary[rootStem].append(
[stem, tag, ' '.join(enGloss.split(';'))])
return dictionary
def verify(self, word):
if self.stopWordsIndex.access(word):
return True
def setStopWordsIndex(self, index: ArabicStopWordsIndex):
self.stopWordsIndex = index
self.stopWordsIndex.buildIndex()
bigram_data = [bigrams_model[doc] for doc in no_stopwords_data]
print(bigram_data[0:2])
############# (5)lemmatizing the data #############
# produces a list of lists of the data lemmatized ... the lemmatizer does not work well when lemmatizing suffixes
stemmer = ISRIStemmer()
lemmatized_data = []
for items in bigram_data:
lemmas = []
for token in items:
token = stemmer.pre32(
token) # removes the three-letter and two-letter prefixes
token = stemmer.suf32(
token) # removes the three-letter and two-letter suffixes
token = stemmer.norm(token, num=1) # removes diacritics
lemmas.append(token)
lemmatized_data.append(lemmas)
print(lemmatized_data[0:2])
############# (5) Preparing the data using gensim for the Model #############
# the preprocess using gensim involves buidling the dictionary, the corpus and the bigrams
# the data is (data_after_lemmatization) and it is a list of lists
# The Dictionary
dictionary = corpora.Dictionary(lemmatized_data)
# the corpus
corpus = [dictionary.doc2bow(d) for d in lemmatized_data]
print(corpus[0:2])
def remove_diacritics(text): arstemmer = ISRIStemmer() result = arstemmer.norm(text, num=1) # remove diacritics which representing Arabic short vowels return result
def remove_diacritics(text):
arstemmer = ISRIStemmer()
result = arstemmer.norm(
text,
num=1) # remove diacritics which representing Arabic short vowels
return result
