def mainScript():
wb1 = openpyxl.load_workbook('contentData.xlsx')
wb2 = openpyxl.load_workbook('commentData.xlsx')
worksheet1 = wb1.active
worksheet2 = wb2.active
number_of_round = (worksheet1.max_row + 1)
fields_list = []
for x in range(2,number_of_round):
# print("round????????????????????????????????????????????????????????????????????????????????????????? ")
vid = worksheet1.cell(row=x, column=2).value
main_list = []
post_date = ""
for row in worksheet2.iter_rows():
if row[0].value == vid:
row_list = []
count = 1
for cell in row:
row_list.append(worksheet2.cell(row=cell.row, column=count).value)
post_date = worksheet2.cell(row=cell.row, column=2).value
count +=1
main_list.append(row_list)
print('your lis ',main_list)
dataset = []
comments_date = []
comment_id = []
for li in main_list:
dataset.append(li[3])
comments_date.append(li[8])
comment_id.append(li[2])
content_string = worksheet1.cell(row=x, column=1).value
dataset.insert(0,content_string)
N = len(dataset)
def calculate_time_gap(val):
rep1 = post_date.replace("Z", "+00:00")
rep2 = val.replace("Z", "+00:00")
x = datetime.fromisoformat(rep1).timestamp()
y = datetime.fromisoformat(rep2).timestamp()
dif = (y-x)/60 # calculate from minitues
return dif
def generate_ngrams(text):
text = re.sub(r'[#$%&()*+-./:;<=>?\s]', '',text)
tokens = word_tokenize(text)
print(tokens) # ['මචං', 'මැසේජ්', 'වගේ']
list_comment = []
for token in tokens:# take a one word at a time
n = len(token)
list_word = []
for i in range(0,(n-1)):
bi = token[i:i+2]
list_word.append(bi)
print("word bi grams list ",list_word)
list_comment.extend(list_word)
print("comment bi grams ",list_comment)
return list_comment
bi_gram_processed_text = [] #this is a list in list which have bi-gram strings duplicate bi-grams also here
words_processed_text = [] # this is a list in list which have words tokens strings duplicate words also here
for text in dataset[:N]:
text_preprocessed_bi_gram = preprocess(text) # remove stop words and punctuations for make bi grams for find similerity
bi_grams = generate_ngrams(text_preprocessed_bi_gram)# make a character bigram list for a comment
bi_gram_processed_text.append(bi_grams)# make a list in list of bigrams for each comments
words_processed_text.append(word_tokenize(str(remove_punctuation(text))))
vectors_list = vectorize(bi_gram_processed_text)
#---------------------------------------------------------------------------------------
center_vector = np.zeros((len(vectors_list[2])))
for numb in range(1,len(vectors_list)):
center_vector = center_vector + vectors_list[numb]
new_center_vector = center_vector/(len(vectors_list)-1)
content_vector = vectors_list[0]
for i in range(1, N):
cos_simillerity_content_comment = calculate_content_comment_similerity(content_vector,vectors_list[i].tolist())
cos_simillerity_comment_comment = calculate_content_comment_similerity(new_center_vector,vectors_list[i].tolist())
word_count =len(words_processed_text[i])
duplicate_word_ratio = duplicate_words(words_processed_text[i])
no_of_sentences = no_sentences(dataset[i])
length_of_comment = com_length(dataset[i])
num_of_punctuations = no_punctuations(dataset[i])
stop_word_ratio = count_stop_word(words_processed_text[i])
post_coment_gap = calculate_time_gap(comments_date[i-1])
is_black_word = check_black_words_list(words_processed_text[i])
link_mail_pnumber = find_mail_site_pnumber(dataset[i])
comment_duplication = find_duplicate_comments(main_list , i-1)
comment_id_val = comment_id[i-1]
# =============================================================================
# print(dataset[i])
# print("cos_simillerity_content_comment ",cos_simillerity_content_comment)
# print("cos_simillerity_comment_comment ",cos_simillerity_comment_comment)
# print("word_count ",word_count)
# print("duplicate_word_ratio ",duplicate_word_ratio)
# print("no_of_sentences ",no_of_sentences)
# print("length_of_comment ",length_of_comment)
# print("num_of_punctuations ",num_of_punctuations)
# print("stop_word_ratio ",stop_word_ratio)
# print("post_coment_gap ",post_coment_gap)
# print("is_black_word ",is_black_word)
# print("link_mail_pnumber ",link_mail_pnumber)
# print("comment_duplication ",comment_duplication)
# =============================================================================
# ['BHhl75a5bks', '2021-01-29T14:34:46Z', 'Ugw5ej-XdiFL40uu9814AaABAg', 'හැමදාම unea ඔහොම තමා', 'UCh3O7jnH1dspTmxbkW4K6yA', 'ශ්\u200dරී ලංකා ක්\u200dරිකට් තවත් කැපිල්ලක්..', 0, '2021-01-30T16:12:28Z', '2021-01-30T16:12:28Z']
stringList = makes_csv(comment_id_val, cos_simillerity_content_comment,cos_simillerity_comment_comment,word_count,duplicate_word_ratio,no_of_sentences,length_of_comment,num_of_punctuations,stop_word_ratio,post_coment_gap,is_black_word,link_mail_pnumber,comment_duplication)
fields_list.append(stringList)
create_file(fields_list)
#mainScript()
def mainScript():
wb1 = openpyxl.load_workbook('contents.xlsx')
wb2 = openpyxl.load_workbook('comments.xlsx')
df = pd.read_excel('comments.xlsx')
li_id = df['comment_id'].tolist()
worksheet1 = wb1.active
worksheet2 = wb2.active
number_of_round = (worksheet1.max_row + 1)
fields_list = []
for x in range(2, number_of_round):
# print("round????????????????????????????????????????????????????????????????????????????????????????? ")
vid = worksheet1.cell(row=x, column=2).value
main_list = []
post_date = ""
for row in worksheet2.iter_rows():
if row[0].value == vid:
row_list = []
count = 1
for cell in row:
row_list.append(
worksheet2.cell(row=cell.row, column=count).value)
post_date = worksheet2.cell(row=cell.row, column=2).value
count += 1
main_list.append(row_list)
dataset = []
comments_date = []
#classifier = []
for li in main_list:
dataset.append(li[3])
comments_date.append(li[8])
#classifier.append(li[9])
content_string = worksheet1.cell(row=x, column=1).value
dataset.insert(0, content_string)
N = len(dataset)
def calculate_time_gap(val):
# rep1 = post_date.replace("Z", "+00:00")
#rep2 = val.replace("Z", "+00:00")
#x = datetime.fromisoformat(rep1).timestamp()
# y = datetime.fromisoformat(rep2).timestamp()
#dif = (y-x)/60 # calculate from minitues
#return dif
obj1 = datetime.strptime(post_date, '%Y-%m-%dT%H:%M:%SZ')
obj2 = datetime.strptime(val, '%Y-%m-%dT%H:%M:%SZ')
time_delta = (obj2 - obj1)
total_seconds = time_delta.total_seconds()
#minutes = total_seconds/60
return total_seconds
def generate_ngrams(text):
text = re.sub(r'[#$%&()*+-./:;<=>?\s]', '', text)
tokens = word_tokenize(text)
# print(tokens) # ['මචං', 'මැසේජ්', 'වගේ']
list_comment = []
for token in tokens: # take a one word at a time
n = len(token)
list_word = []
for i in range(0, (n - 1)):
bi = token[i:i + 2]
list_word.append(bi)
#print("word bi grams list ",list_word)
list_comment.extend(list_word)
#print("comment bi grams ",list_comment)
return list_comment
# claculate peiods sequence
def calc(st):
x = ''
y = ''
for i in st:
y = x
x = i
if x == '.' and y == '.':
return 1
#print(i)
return 0
bi_gram_processed_text = [
] #this is a list in list which have bi-gram strings duplicate bi-grams also here
words_processed_text = [
] # this is a list in list which have words tokens strings duplicate words also here
for text in dataset[:N]:
text_preprocessed_bi_gram = preprocess(
text
) # remove stop words and punctuations for make bi grams for find similerity
bi_grams = generate_ngrams(
text_preprocessed_bi_gram
) # make a character bigram list for a comment
bi_gram_processed_text.append(
bi_grams) # make a list in list of bigrams for each comments
words_processed_text.append(
word_tokenize(str(remove_punctuation(text))))
vectors_list = vectorize(bi_gram_processed_text)
#---------------------------------------------------------------------------------------
#center_vector = np.zeros((len(vectors_list[2]))) # initialize for comments center vector
dynamic_center_vector = []
for numb in range(1, len(vectors_list)):
center_vector = np.zeros((len(
vectors_list[0]))) # initialize for comments center vector
count = 0
for i in vectors_list[1:]:
count += 1
if count == numb:
continue
else:
center_vector = np.add(center_vector, i)
row_center_vector = center_vector / (len(vectors_list) - 2)
dynamic_center_vector.append(row_center_vector)
content_vector = vectors_list[0]
for i in range(1, N):
cos_simillerity_content_comment = calculate_content_comment_similerity(
content_vector, vectors_list[i].tolist())
cos_simillerity_comment_comment = calculate_content_comment_similerity(
dynamic_center_vector[i - 1], vectors_list[i].tolist())
#word_count =len(words_processed_text[i])
duplicate_word_ratio = duplicate_words(words_processed_text[i])
no_of_sentences = no_sentences(dataset[i])
#length_of_comment = com_length(dataset[i])
num_of_punctuations = no_punctuations(dataset[i])
is_period_sequence = calc(dataset[i])
stop_word_ratio = count_stop_word(words_processed_text[i])
post_coment_gap = calculate_time_gap(comments_date[i - 1])
#is_black_word = check_black_words_list(words_processed_text[i])
#link_mail_pnumber = find_mail_site_pnumber(dataset[i])
comment_duplication = find_duplicate_comments(main_list, i - 1)
link_mob_mail_word_length = link_mob_mail_length_blckword(
words_processed_text[i])
#classifier_val = classifier[i-1]
print(' ')
print(dataset[i])
print("cos_simillerity_content_comment ",
cos_simillerity_content_comment)
print("cos_simillerity_comment_comment ",
cos_simillerity_comment_comment)
print("word_count ", link_mob_mail_word_length[1])
print("duplicate_word_ratio ", duplicate_word_ratio)
print("no_of_sentences ", no_of_sentences)
print("length_of_comment ", link_mob_mail_word_length[0])
print("num_of_punctuations ", num_of_punctuations)
print("is period sequence ", is_period_sequence)
print("stop_word_ratio ", stop_word_ratio)
print("post_coment_gap ", post_coment_gap)
print("black_word_ratio ", link_mob_mail_word_length[-1])
print("is_link ", link_mob_mail_word_length[2])
print("is_youtube_link ", link_mob_mail_word_length[3])
print("is_number ", link_mob_mail_word_length[4])
#print("is_mail ",link_mob_mail_word_length[5])
print("comment_duplication ", comment_duplication)
stringList = makes_csv(
li_id[i - 1], cos_simillerity_content_comment,
cos_simillerity_comment_comment, link_mob_mail_word_length[1],
duplicate_word_ratio, no_of_sentences,
link_mob_mail_word_length[0], num_of_punctuations,
is_period_sequence, stop_word_ratio, post_coment_gap,
link_mob_mail_word_length[-1], link_mob_mail_word_length[2],
link_mob_mail_word_length[3], link_mob_mail_word_length[4],
comment_duplication)
fields_list.append(stringList)
create_file(fields_list)
center_vector = np.add(center_vector, i)
row_center_vector = center_vector / (len(vectors_list) - 2)
dynamic_center_vector.append(row_center_vector)
content_vector = vectors_list[0]
for i in range(1, N):
cos_simillerity_content_comment = calculate_content_comment_similerity(
content_vector, vectors_list[i].tolist())
cos_simillerity_comment_comment = calculate_content_comment_similerity(
dynamic_center_vector[i - 1], vectors_list[i].tolist())
#word_count =len(words_processed_text[i])
duplicate_word_ratio = duplicate_words(words_processed_text[i])
no_of_sentences = no_sentences(dataset[i])
#length_of_comment = com_length(dataset[i])
num_of_punctuations = no_punctuations(dataset[i])
is_period_sequence = calc(dataset[i])
#stop_word_ratio = count_stop_word(words_processed_text[i])
post_coment_gap = calculate_time_gap(comments_date[i - 1])
#is_black_word = check_black_words_list(words_processed_text[i])
#link_mail_pnumber = find_mail_site_pnumber(dataset[i])
comment_duplication = find_duplicate_comments(main_list, i - 1)
link_mob_mail_word_length = link_mob_mail_length_blckword(
words_processed_text[i])
classifier_val = classifier[i - 1]
print(' ')
print(dataset[i])
def mainScript():
wb1 = openpyxl.load_workbook('contents.xlsx')
wb2 = openpyxl.load_workbook('comments.xlsx')
df = pd.read_excel('comments.xlsx')
li_id = df['comment_id'].tolist()
worksheet1 = wb1.active
worksheet2 = wb2.active
number_of_round = (worksheet1.max_row + 1)
fields_list = []
for x in range(2,number_of_round):
vid = worksheet1.cell(row=2, column=2).value
main_list = []
post_date = ""
for row in worksheet2.iter_rows():
if row[0].value == vid:
row_list = []
count = 1
for cell in row:
row_list.append(worksheet2.cell(row=cell.row, column=count).value)
post_date = worksheet2.cell(row=cell.row, column=2).value
count +=1
main_list.append(row_list)
dataset = []
comments_date = []
for li in main_list:
dataset.append(li[3])
comments_date.append(li[8])
content_string = worksheet1.cell(row=2, column=1).value
dataset.insert(0,content_string)
N = len(dataset)
def de_emojies(text1):
regrex_pattern = re.compile(pattern = "["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags = re.UNICODE)
text = regrex_pattern.sub(r' ඉමොජි ',text1)
return text
new_dataset = []
def remove_emojies(dataset):
d_list = dataset
for one_list in d_list:
clean_text = de_emojies(one_list)
new_dataset.append(clean_text)
remove_emojies(dataset)
def calculate_time_gap(val):
obj1 = datetime.strptime(post_date,'%Y-%m-%dT%H:%M:%SZ')
obj2 = datetime.strptime(val,'%Y-%m-%dT%H:%M:%SZ')
time_delta = (obj2-obj1)
total_seconds = time_delta.total_seconds()
#minutes = total_seconds/60
return total_seconds
def generate_ngrams(text):
text = re.sub(r'[#$%&()*+-./:;<=>?\s]', '',text)
tokens = word_tokenize(text)
# print(tokens) # ['මචං', 'මැසේජ්', 'වගේ']
list_comment = []
for token in tokens:# take a one word at a time
n = len(token)
list_word = []
for i in range(0,(n-1)):
bi = token[i:i+2]
list_word.append(bi)
list_comment.extend(list_word)
return list_comment
# claculate peiods sequence
def calc(st):
x = ''
y = ''
for i in st:
y = x
x = i
if x=='.'and y=='.':
return 1
return 0
bi_gram_processed_text = [] #this is a list in list which have bi-gram strings duplicate bi-grams also here
words_processed_text = [] # this is a list in list which have words tokens strings duplicate words also here
for text in new_dataset[:N]:
text_preprocessed_bi_gram = preprocess(text) # remove stop words and punctuations for make bi grams for find similerity
bi_grams = generate_ngrams(text_preprocessed_bi_gram)# make a character bigram list for a comment
bi_gram_processed_text.append(bi_grams)# make a list in list of bigrams for each comments
words_processed_text.append(word_tokenize(str(remove_punctuation(text))))
vectors_list = vectorize(bi_gram_processed_text)
#center_vector = np.zeros((len(vectors_list[2]))) # initialize for comments center vector
dynamic_center_vector = []
for numb in range(1,len(vectors_list)):
center_vector = np.zeros((len(vectors_list[0]))) # initialize for comments center vector
count = 0
for i in vectors_list[1:]:
count +=1
if count == numb:
continue
else:
center_vector = np.add(center_vector,i)
row_center_vector = center_vector/(len(vectors_list)-2)
dynamic_center_vector.append(row_center_vector)
content_vector = vectors_list[0]
for i in range(1, N):
cos_simillerity_content_comment = calculate_content_comment_similerity(content_vector,vectors_list[i].tolist())
cos_simillerity_comment_comment = calculate_content_comment_similerity(dynamic_center_vector[i-1],vectors_list[i].tolist())
duplicate_word_ratio = duplicate_words(words_processed_text[i])
no_of_sentences = no_sentences(new_dataset[i])
num_of_punctuations = no_punctuations(new_dataset[i])
is_period_sequence = calc(new_dataset[i])
post_coment_gap = calculate_time_gap(comments_date[i-1])
comment_duplication = find_duplicate_comments(main_list , i-1)
link_mob_mail_word_length = link_mob_mail_length_blckword(words_processed_text[i])
stringList = makes_csv(li_id[i-1], cos_simillerity_content_comment,cos_simillerity_comment_comment,link_mob_mail_word_length[1],duplicate_word_ratio,no_of_sentences,link_mob_mail_word_length[0],num_of_punctuations,is_period_sequence,post_coment_gap,link_mob_mail_word_length[-1],link_mob_mail_word_length[2],link_mob_mail_word_length[3],link_mob_mail_word_length[4],comment_duplication)
fields_list.append(stringList)
create_file(fields_list)
#mainScript()