def Text_Cleanup(f):
text = f.lower()
text_contraction = cont.fix(text)
text_punc = text_contraction.translate(translator)
text_clean = ' '.join([word for word in text_punc.split() if(len(word.lower())>1)])
text_sent = ' '.join([i for i in text_clean.split() if(i.isalnum() and not i.isdigit())])
return text_sent
def replaceContractions(self, text): """Replace contractions in string of text""" return contractions.fix(text)
x23 = x22.replace(":-/", "sad")
x24 = x23.replace(":/", "sad")
x25 = x24.replace(":|", "sad")
return x25
df['emoticons_replacment'] = df['textOriginal'].apply(smiley)
#-----------------------------------------------------------------------------------------------------------------------
df["less_spaces"] = df['emoticons_replacment'].apply(
lambda x: re.sub(' +', ' ', x))
#https://towardsdatascience.com/preprocessing-text-data-using-python-576206753c28
df['text_expan_contractions'] = df['less_spaces'].apply(
lambda x: [contractions.fix(word) for word in x.split()])
df['text_expan_contractions'] = [
' '.join(map(str, l)) for l in df['text_expan_contractions']
]
#removes non alphanumeric/ whitespace characters from strings
df['text_misc_char_removed'] = df['text_expan_contractions'].str.replace(
''', '') # just a lil something to replace the weird apostroph thing
df['text_misc_char_removed'] = df['text_misc_char_removed'].map(
lambda x: re.sub("[^0-9a-zA-Z\s]+", '', x)
) #this includes puncutation which shoes little value in analysis
#removes emojis
def deEmojify(text):
def con(text): expand = contractions.fix(text) return expand
def expand_contractions(text):
""" expand shortened words, e.g. don't to do not """
return contractions.fix(text)
def replace_contractions(df):
# return contractions.fix(t)
# df['text_prep'] = df.text_prep.apply(lambda x: nltk.word_tokenize(contractions.fix(TreebankWordDetokenizer().detokenize(x))))
df['text_prep'] = df.text_prep.apply(lambda x: contractions.fix(x))
print('contractions expansion done')
return df
def convert_emoticons(text):
for emot, desc in EMOTICONS.items():
text = re.sub(u'(' + emot + ')', desc, text)
return text
udf_convert_emoticons = udf(convert_emoticons)
def convert_contractions(text):
return contractions.fix(text)
# udf_convert_contractions = udf(convert_contractions)
udf_convert_contractions = udf(lambda text: contractions.fix(text))
def convert_numbers_to_text(text):
return ' '.join([num2words(w) if w.isdigit() else w for w in text.split()])
# udf_convert_numbers_to_text = udf(convert_numbers_to_text, ArrayType(StringType()))
udf_convert_numbers_to_text = udf(
lambda text: ' '.join(
[num2words(w) if w.isdigit() else w for w in text.split()]),
ArrayType(StringType()))
def convert_date_to_text(text):
result = []
def test_add():
contractions.add('mychange', 'my change')
assert contractions.fix('mychange') == 'my change'
def data_cleaning(df):
info = [df['Sentiment'].values.tolist(), df['Text'].values.tolist()]
df_data = list(zip(*info))
clean_data = []
sentiment = []
pos = {}
neg = {}
counts = {}
for i in range(0, len(df_data)):
if int(df_data[i][0]) == 1:
sentiment.append(1)
if int(df_data[i][0]) == -1:
sentiment.append(-1)
#1) remove emails
clean_sentence = re.sub(r'\s*\S*(@)\S*', '', str(df_data[i][1]))
#remove mentions
clean_sentence = re.sub(r"([@][\w_-]+)", "", clean_sentence)
#2) remove 10 digit phone numbers
#clean_sentence = re.sub(r'\d{10}', '', clean_sentence)
#3) remove $n
clean_sentence = re.sub(r'\$[^ ]+', '', clean_sentence)
#4) remove Times & dates 2/24 2:10pm, 6/30, 7:00 AM
clean_sentence = re.sub(
r'[0-9]*[:/][0-9]*\S*\s[A][M]|[0-9]*[:/][0-9]*\S*\s[P][M]|[0-9]*[:/][0-9]*\S*',
'', clean_sentence)
#5) convert emojis
clean_sentence = emoji.demojize(clean_sentence, delimiters=(' ', ' '))
#6) fix contractions
clean_sentence = contractions.fix(clean_sentence)
#7) remove links
clean_sentence = re.sub(r'\s*\S*(http)\S*', '', clean_sentence)
#8) keeping the hashtage info but removing the sign
clean_sentence = clean_sentence.replace("#", "")
#9) all to lower case for easy tokenization and less features
#clean_sentence = clean_sentence.lower()
#10) remove < >
clean_sentence = clean_sentence.replace("<", "")
clean_sentence = clean_sentence.replace(">", "")
#11) remove Punctuations
clean_sentence = re.sub(r'[^A-Za-z0-9]+', ' ', clean_sentence)
#12) lemmatize verbs
tokenized = word_tokenize(clean_sentence)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
for word in tokenized:
cur = lemmatizer.lemmatize(word, pos='v')
clean_tokens.append(cur)
#13) remove stop words
stop_words = set(stopwords.words('english'))
filtered_sentence = [w for w in clean_tokens if not w in stop_words]
clean_data.append(filtered_sentence)
for word in filtered_sentence:
if sentiment[i] == 1:
if word in pos and not word.isupper():
pos[word] += 1
else:
pos[word] = 1
else:
if word in neg and not word.isupper():
neg[word] += 1
else:
neg[word] = 1
if word not in counts:
counts[word] = 1
else:
counts[word] += 1
neg = {key: val for key, val in neg.items() if val != 1}
pos = {key: val for key, val in pos.items() if val != 1}
alldata = {key: val for key, val in counts.items() if val != 1}
#print(clean_data[5])
#print(len(sentiment) == len(clean_data))
f = open("pos.txt", "w")
f.write(str(pos))
f.close()
f = open("neg.txt", "w")
f.write(str(neg))
f.close()
f = open("counts.txt", "w")
f.write(str(alldata))
f.close()
a_dictionary = dict(Counter(counts).most_common(50))
keys = a_dictionary.keys()
values = a_dictionary.values()
plt.xticks(fontsize=6)
plt.bar(keys, values, color='pink')
plt.show()
#check for duplicates
new_clean_data = []
for i in range(0, len(clean_data)):
cur_data = (' '.join(clean_data[i]), sentiment[i])
if cur_data not in new_clean_data:
#new_clean_data.append(clean_data[i])
#new_senti.append(sentiment[i])
new_clean_data.append(cur_data)
#print(new_clean_data[:3])
return new_clean_data
def contraction_expansion(text):
text = contractions.fix(text)
return text
def remove_contractions(x):
rem_cont = [contractions.fix(word) for word in x.split()]
return " ".join(map(str, rem_cont))
def standardised_query(pl, text):
text = remove_punctuation(text)
text = contractions.fix(text)
text = lemmatise(text)
return escape_and_call_prolexa(pl, text)
def get_token_words(survey_data, col_name, stopwords_list, title):
""" Return a list of all the token words that we can use to
generate the bigrams.
survey_data: Pandas data frame, contains data to analyze
col_name: Name of specific column to analyze responses
stopwords_list: List of words to ignore that might be in the question
title: Title of sentiment
"""
# Drop null values, reset the index
print(survey_data.columns)
data = survey_data.dropna(subset=[col_name])
data = data.reset_index(drop=True)
# Get only the column that you need
responses_data = data[col_name]
# Make it a list without the col name
responses = []
for i in range(len(responses_data) - 1):
responses.append(str(responses_data[i + 1]))
sentiment_list = sentiment(responses)
sent = []
for i in range(len(sentiment_list)):
if sentiment_list[i] != "n/a":
if sentiment_list[i] > 0:
sent.append("pos")
elif sentiment_list[i] == 0:
sent.append("neutral")
else:
sent.append("neg")
else:
sent.append("n/a")
sentiment_table = pd.DataFrame({'sentiment': sentiment_list})
sentiment_table['sent_word'] = sent
sentiment_table['responses'] = responses
sentiment_table.to_csv("files/Neut_" + title, index=False)
processed = []
index = 0
while index < len(responses):
# Look at words in one response
# lowercase
responsewords = responses[index].lower()
# remove punctuation
responsewords = re.sub('[!#?,.:";\']', "", responsewords)
# split into a list
resultwords = responsewords.split()
# expand contractions
for i in range(len(resultwords)):
word = resultwords[i]
resultwords[i] = contractions.fix(word)
# remove stopwords
for word in stopwords_list:
if word in resultwords:
resultwords.remove(word)
# Joing back as text
processed.append(" ".join(resultwords))
index += 1
# Join the responses into one big text
text = " ".join(processed)
# Stemming: removes suffices: ing, ly, s
# Lemmatization (root word)
st = PorterStemmer()
lemmatizer = WordNetLemmatizer()
text_stem = []
for word in text.split(" "):
word = st.stem(word)
text_stem.append(lemmatizer.lemmatize(word))
text = " ".join(text_stem)
# Passing the string text into word tokenize for breaking the sentences
tokens = word_tokenize(text)
# Word cloud
#wordcloud = WordCloud(background_color="white").generate(text)
#plt.imshow(wordcloud, interpolation='bilinear')
#plt.axis("off")
#plt.show()
# Removing english stopwords
eng_stopwords = set(stopwords.words("english"))
tokens = [w for w in tokens if w not in eng_stopwords]
return tokens
def replace_contractions(text):
"""Replaces contractions (it's -> it is)"""
return contractions.fix(text)
def fix_contractions(s):
s = contractions.fix(s)
return s
wordset = set() # initialise empty set of words
bodies = list() # initialise empty set to contain bodies of text gathered
with open('text.txt', 'w') as f:
for tag_idx in range(1, 174):
print(tags[tag_idx])
href = tags[tag_idx].attrs['href']
page = urllib.request.urlopen(site + href)
page_soup = bs(page, 'html.parser')
#print(page_soup.prettify())
text = page_soup.find('font', face='verdana')
#print(str(text))
print('\n\n\n')
text = text.text # get just the text (remove tags etc)
f.write(text)
text = re.sub('\[[^]]*\]', '', text) # remove square brackets
text = contractions.fix(text) # replaced contractions with their full words
words = nltk.word_tokenize(text) # make list of word tokens
words = [word.lower() for word in words] # lowercase
words = [re.sub(r'[^\w\s]', '', word) for word in words] # replace punctuation with empty string
words = [word for word in words if word != ''] # remove empty strings
bodies.append(words)
print(bodies)
wordset = wordset.union(words) # add any new words in this body to the set of words
break
print('Number of individual words:', len(wordset))
#print(text)
print(words)
def test_fix():
assert contractions.fix("you're happy now") == "you are happy now"
def replace_contractions(text, verbose=False):
"""Replace contractions in string of text"""
new_text = contractions.fix(text)
if verbose:
print(new_text)
return new_text
def remove_contractions(text):
return contractions.fix(text)
def replace_contractions(self, text):
return contractions.fix(text)
def convert_contractions(text):
return contractions.fix(text)
def preprocess(temp):
# expand using contractions
temp = re.sub(r"(http|https)\S+", "", temp)
temp = contractions.fix(temp)
# tokenize
tokens = nltk.word_tokenize(temp)
#tokens = tokenizer.tokenize(temp)
# for i,token in enumerate(tokens):
# if token[0].isupper():
# print("{}:{}:{}".format(i,file,token))
string.punctuation = string.punctuation + "''``--"
#print(tokens)
new_tokens = []
pattern = ('\d+(\.\d+)?')
for i, token in enumerate(tokens):
if i == len(tokens):
break
if (token == '@'):
#replace twitter handle with screen name
temp = tokens[i] + tokens[i + 1]
#print(temp)
if get_username(temp) is not None:
#print(get_username(temp))
temp_list = nltk.word_tokenize(get_username(temp))
for t in temp_list:
new_tokens.append(t.lower())
i = i + 2
continue
if len(token) < 3:
continue
if token in english_stopwords:
continue
if (token not in string.punctuation):
if not re.match(pattern, token):
for s in token:
if s in string.punctuation:
token = token.replace(s, '')
else:
if token.isdigit():
token = num2words(float(token))
new_tokens.append(token.lower().encode("ascii",
errors="ignore").decode())
'''
# lemmitization
lemmatizer = nltk.WordNetLemmatizer()
final_tokens=[]
for token in new_tokens:
final_tokens.append(lemmatizer.lemmatize(token))
# stemmer
stemmer = nltk.PorterStemmer()
final_tokens = []
for token in new_tokens:
final_tokens.append(stemmer.stem(token))
'''
return new_tokens
start_time = time.time()
vocab_full = {}
n_doc = 0
# Only keep the data dictionaries and ignore possible system files like .DS_Store
folders = [
os.path.join(root_path, name) for name in os.listdir(root_path)
if os.path.isdir(os.path.join(root_path, name))
]
for folder in folders:
for filename in os.listdir(folder):
file = os.path.join(folder, filename)
n_doc += 1
with open(file, 'r', encoding='utf8', errors='ignore') as f:
for line in f:
# split contractions into two words
line = contractions.fix(line)
tokens = word_tokenize(line)
# force everything to lower case and remove non-alphabetic characters
tokens = [token.lower() for token in tokens if token.isalpha()]
for token in tokens:
# remove stop words, other words (above) and single characters
if (token not in stop_words) and (
token not in other_words) and (len(token) > 1):
vocab_full[token] = vocab_full.get(token, 0) + 1
print(
f'{n_doc} documents in total with a total vocab size of {len(vocab_full)}')
vocab_sorted = sorted(vocab_full.items(),
key=operator.itemgetter(1),
reverse=True)
vocab_truncated = vocab_sorted[:MAX_VOCAB_SIZE]
# Save the vocabulary to file for visual inspection and possible analysis
def expand_contractions(text):
text_uncont = contractions.fix(text)
return text_uncont
def remove_html_tags(text):
soup = BeautifulSoup(text, "html.parser")
text = soup.get_text()
text = contractions.fix(text)
return text
def expand_contraction(input_text: str) -> str:
""" Expand contractions in input text """
return contractions.fix(input_text)
def replace_contractions(txt):
return contractions.fix(txt)
def replace_contractions(text):
return (contractions.fix(text))
def replaceContractions(self, text):
"""Replace contractions in string of text"""
return contractions.fix(text)
def remove_contractions(k):
return k.apply(lambda x: contractions.fix(x)) # don't - do not
def expand_contractions(text):
return contractions.fix(text)
def _replace_contractions(text):
"""Replace contractions in string of text"""
return contractions.fix(text)
