def preprocessing(conversations):
"""
Word Stemming and Stop words Removal
"""
pred_conversations = []
for i in range(len(conversations)):
# Remove unique chars
conversation = ""
for j in range(len(conversations[i])):
if ord(conversations[i][j]) < 128:
conversation += conversations[i][j]
sentence = []
# word tokenize
words = word_tokenize(conversation)
removal = "?!.,( )"
stop_words = set(stopwords.words('english'))
stop_words.update(("'s", "n't", "'m", "'ve", "'re", "'d", "'"))
for word in words:
# Remove ?!.,
pred_word = ""
for j in range(len(word)):
if word[j] in removal: continue
pred_word += word[j]
# Lower, word stemming and stop words removal
if len(pred_word) != 0:
pred_word = SnowballStemmer("english").stem(pred_word.lower())
if pred_word in stop_words: continue
sentence.append(pred_word)
pred_conversations.append(sentence)
return pred_conversations
def morphy_stem(word):
"""
Simple stemmer
"""
#stem = wn.morphy(word)
stem = SnowballStemmer('english').stem(word)
#stem = lemma.lemmatize(stem2)
if stem:
return stem.lower()
else:
return word.lower()
def get_data(name_train, name_test_closed, name_test_open):
PATH_TRAINING = name_train
PATH_TEST = name_test_open
train_set = pd.read_csv(PATH_TRAINING,
header=0,
sep=',',
quotechar='"',
names=['autor', 'gender', 'age', 'text'])
test_set = pd.read_csv(PATH_TEST,
header=0,
sep=',',
quotechar='"',
names=['autor', 'gender', 'age', 'text'])
# train_set = train_set.groupby('autor').agg({'gender': 'first',
# 'age': 'first',
# 'text': ' '.join}).reset_index()
punct = set(punctuation)
punct_cleaned = set(punctuation.replace("-", "").replace("'", ""))
for i in range(len(train_set)):
old_text = train_set["text"].iloc[i]
# split into sentences
sentences = re.split('[.!?]', old_text)
sentences_clean = []
# for each sentences
for sentence in sentences:
sentence_new = []
for word in sentence.split():
if word not in punct_cleaned:
word = ''.join(ch for ch in word
if ch not in punct_cleaned)
word = word.lower()
word = SnowballStemmer('english').stem(word)
if word.isnumeric():
word = "NUMBER"
sentences_clean.append(word)
train_set["text"].iloc[i] = ' '.join(sentences_clean)
for i in range(len(test_set)):
old_text = test_set["text"].iloc[i]
# split into sentences
sentences = re.split('[.!?]', old_text)
sentences_clean = []
# for each sentences
for sentence in sentences:
sentence_new = []
for word in sentence.split():
if word not in punct_cleaned:
word = ''.join(ch for ch in word
if ch not in punct_cleaned)
word = word.lower()
word = SnowballStemmer('english').stem(word)
if word.isnumeric():
word = "NUMBER"
sentences_clean.append(word)
test_set["text"].iloc[i] = ' '.join(sentences_clean)
return train_set, test_set
