def delete_dataStopword(self):
id = request.form['id']
instance_Model = Models(
'DELETE FROM tbl_stopword WHERE id_stopword = %s')
instance_Model.query_sql(id)
flash('Berhasil menghapus data.', 'success')
return None
def add_dataLabeling(self):
id = request.form['id']
value = request.form['value']
data_ubah = (value, id)
instance_Model = Models('UPDATE tbl_tweet_clean SET sentiment_type=%s WHERE id=%s')
instance_Model.query_sql(data_ubah)
return 'Berhasil Melabeli Data!'
def add_dataStopword(self):
stopword = request.form['stopword'].strip()
instance_Model = Models(
'INSERT INTO tbl_stopword(stopword) VALUES (%s)')
instance_Model.query_sql(stopword.lower())
flash('Berhasil menambahkan data.', 'success')
return None
def delete_dataPositiveWord(self):
id = request.form['id']
instance_Model = Models(
'DELETE FROM tbl_lexicon_positive WHERE id_positive = %s')
instance_Model.query_sql(id)
flash('Berhasil menghapus data.', 'success')
return None
def add_dataPositiveWord(self):
kata_positif = request.form['kata_positif'].strip()
instance_Model = Models(
'INSERT INTO tbl_lexicon_positive(positive_word) VALUES (%s)')
instance_Model.query_sql(kata_positif.lower())
flash('Berhasil menambahkan data.', 'success')
return None
def update_dataStopword(self):
id = request.form['id']
stopword = request.form['stopword'].strip()
data_ubah = (stopword.lower(), id
) # Membuat tupple dari form data masukan
instance_Model = Models(
'UPDATE tbl_stopword SET stopword=%s WHERE id_stopword = %s')
instance_Model.query_sql(data_ubah)
flash('Berhasil mengubah data.', 'success')
return None
def add_dataSlangword(self):
slangword = request.form['slangword'].strip()
kata_asli = request.form['kata_asli'].strip()
data_tambah = (slangword.lower(), kata_asli.lower()
) # Membuat tupple dari form data masukan
instance_Model = Models(
'INSERT INTO tbl_slangword(slangword, kata_asli) VALUES (%s,%s)')
instance_Model.query_sql(data_tambah)
flash('Berhasil menambahkan data.', 'success')
return None
def update_dataPositiveWord(self):
id = request.form['id']
kata_positif = request.form['kata_positif'].strip()
data_ubah = (kata_positif.lower(), id
) # Membuat tupple dari form data masukan
instance_Model = Models(
'UPDATE tbl_lexicon_positive SET positive_word=%s WHERE id_positive = %s'
)
instance_Model.query_sql(data_ubah)
flash('Berhasil mengubah data.', 'success')
return None
def delete_dataModelling(self):
id = request.form['id']
instance_Model = Models('DELETE FROM tbl_model WHERE model_name = %s')
instance_Model.query_sql(id)
if os.path.exists('application/static/model_data/' + id):
os.remove('application/static/model_data/' + id)
flash(
'Berhasil menghapus data. File (.json) model latih berhasil dihapus!',
'success')
else:
flash('File (.json) model latih gagal dihapus!', 'error')
print("\nFile tidak ditemukan!\n")
def register(self):
username = request.form['username'].strip()
kata_sandi = request.form['kata_sandi'].strip()
fullname = request.form['fullname'].strip()
# cek record (ada?)
instance_Model = Models(
'SELECT password, fullname FROM tbl_users WHERE username = %s')
user = instance_Model.select_row(username)
# record TIDAK ditemukan
if user == None:
data_tambah = (username, 'textmining_' + str(kata_sandi) +
'_sentimentanalysis', fullname)
# simpan data user
instance_Model = Models(
'INSERT INTO tbl_users(username, password, fullname) VALUES (%s, %s, %s)'
)
instance_Model.query_sql(data_tambah)
flash('Berhasil mendaftar, silakan masuk.', 'success')
return True
flash('Username telah terdaftar.', 'error')
return False
def create_dataModeling(self):
sample_positive = request.form['sample_positive']
sample_negative = request.form['sample_negative']
jumlah_sample = int(sample_positive) + int(sample_negative)
# if sample_positive == sample_negative == sample_netral:
if sample_positive == sample_negative:
list_data = [
] # wadah untuk menyimpan data yang diperoleh dari database
# Select data positif dari tbl_tweet_training sebanyak n record (berdasarkan variabel sample)
instance_Model = Models(
"SELECT clean_text, sentiment_type FROM tbl_tweet_training WHERE clean_text IS NOT NULL AND sentiment_type = 'positif' ORDER BY RAND() LIMIT "
+ sample_positive)
list_data.append(instance_Model.select())
# Select data negatif dari tbl_tweet_training sebanyak n record (berdasarkan variabel sample)
instance_Model = Models(
"SELECT clean_text, sentiment_type FROM tbl_tweet_training WHERE clean_text IS NOT NULL AND sentiment_type = 'negatif' ORDER BY RAND() LIMIT "
+ sample_negative)
list_data.append(instance_Model.select())
teks_list = [
] # wadah untuk tweet (clean_text) yang akan dijadikan sebagai model latih
label_list = [
] # wadah untuk sentimen (sentiment_type) yang akan dijadikan sebagai model latih
# set data untuk teks_list dan label_list menggunakan data yang telah diambil dari database
# for index_luar in range(3):
for index_luar in range(2):
for index_dalam in range(len(list_data[index_luar])):
clean_text = list_data[index_luar][index_dalam][
'clean_text']
sentiment_type = list_data[index_luar][index_dalam][
'sentiment_type']
teks_list.append(clean_text)
label_list.append(sentiment_type)
# akses ke kelas Vectorizer
instance_Vectorizer = Vectorizer(teks_list, label_list)
# membuat vektor angka
data_dict = instance_Vectorizer.create_vectorList()
model_name = 'sentiment_model(' + datetime.today().strftime(
'%d-%m-%Y %H%M%S') + ').json'
# model_name = 'sentiment_model('+ datetime.today().strftime('%d-%m-%Y') +').json'
# Menyimpan model kedalam bentuk .json agar dapat digunakan kembali (untuk proses Evaluasi & Prediksi)
with open('application/static/model_data/' + model_name,
'w') as outfile:
json.dump(data_dict, outfile, indent=4)
# Membuat tuple untuk simpan data
data_simpan = (model_name, jumlah_sample, sample_positive,
sample_negative)
# Insert model ke dalam database
instance_Model = Models(
'REPLACE INTO tbl_model(model_name, sentiment_count, sentiment_positive, sentiment_negative) VALUES (%s, %s, %s, %s)'
)
# Menjadikan tuple sebagai argumen untuk method query_sql
instance_Model.query_sql(data_simpan)
return {
'model_name': model_name,
'sentiment_count': jumlah_sample,
'sentiment_positive': sample_positive,
'sentiment_negative': sample_negative,
'data_dict': data_dict
}
return {'error': 'Gagal Membuat Model Latih'}