def __init__(self):
self.nlp_main = NLP()
Twitter_API.__init__(self, self.nlp_main)
websites_crawler.__init__(self, self.nlp_main)
csv.field_size_limit(256 << 10)
self.geolocator = Nominatim(user_agent="Chain_Anda")
column = ["keyword", "tweet"]
writer = ManageFile("Hit_Trends", "Hit_Trends", column, "w")
# WOEID of Bangkok
woeid = 1225448
# fetching the trends
trends = self.api.trends_place(id=woeid)
# printing the information
print("The top trends for the location are :")
for value in trends:
for trend in value['trends']:
writer.managefile_main([trend["name"], trend["tweet_volume"]])
#print(trend["name"], trend["tweet_volume"])
print(time.time() - start, "hittwitter")
if __name__ == "__main__":
obj = Twitter_API(NLP())
obj.main_twitter("all", 50, "covid", "2021-04-25", "2021-04-29")
#obj.update_mode()
# ref: https://chatbotslife.com/crawl-twitter-data-using-30-lines-of-python-code-e3fece99450e
# ref: http://docs.tweepy.org/en/latest/api.html?highlight=search#search-methods
# ref: https://web.archive.org/web/20170829051949/https://dev.twitter.com/rest/reference/get/search/tweets
# ref: http://docs.tweepy.org/en/latest/extended_tweets.html?highlight=retweet
self.output2 = []
self.output3 = []
self.output4 = []
#self.output_write = [[],[]]
all_t1 = time.time()
cou = len(open("check_data.txt").readlines())
print(
f"time: {all_t1-all_t0} seconds, length: {cou-1} links. 4")
all_time1 = time.time()
cou = len(open("check_data.txt").readlines())
self.check_data.write("time: " + str(all_time1 - all_time0) +
" seconds, length: " + str(cou - 1) +
" link")
self.check_data.flush()
print(
f"time: {all_time1-all_time0} seconds, length: {cou-1} links.")
else:
self.searching(keyword, lang, since, until)
if __name__ == "__main__":
obj = websites_crawler(NLP())
obj.main_crawler("covid", "all", "2021-01-01", "2021-01-01", True)
#get_ = requests.get("https://www.reuters.com/investigates/section/climate-change-scientists/")
#html = get_.content
#obj.find_message(html,"https://www.reuters.com/investigates/section/climate-change-scientists/")
#obj.download_url("https://www.reuters.com/", "www.reuters.com", 2)
# ref: https://realpython.com/python-concurrency/
def setUp(self):
self.nlp = NLP()
self.twitter = Twitter_API(self.nlp)
self.start = time.time()
class main(Twitter_API, websites_crawler, NLP):
def __init__(self):
self.nlp_main = NLP()
Twitter_API.__init__(self, self.nlp_main)
websites_crawler.__init__(self, self.nlp_main)
csv.field_size_limit(256 << 10)
self.geolocator = Nominatim(user_agent="Chain_Anda")
# run combin twitter and website
def run_all(self, lang, count, keyword, since, until):
keyword = keyword.lower()
self.lang = lang
self.count = count
self.keyword = keyword
self.since = since
self.until = until
# ตรงนี้กะว่าจะเอาไว้ตรวจสอบคำแต่พอทำไปทำมา method go_twitter ทำได้ดีกว่า
check = self.check_keyword(keyword)
self.array_sentiment = []
self.array_sentiment_twi = []
self.array_sentiment_web = []
a = time.time()
# Go search twitter and check that keyword has search yet
self.go_twitter(lang, count, keyword, since, until)
print("Time: ", time.time() - a)
print("=" * 10 + "End Find Twitter" + "=" * 10)
# run search crawler
self.main_crawler(keyword, lang, since, until)
# bring file that main_twitter and main_crawler has wrote before to cut text by NLP and sorting word for show on GUI
self.cut_text("WebCrawler", keyword,
["time", "header", "content", "link"], lang, since,
until)
self.cut_text("Twitter", keyword, ["time", "content", "places"], lang,
since, until)
# run twitter
def run_twitter(self, lang, count, keyword, since, until):
keyword = keyword.lower()
self.lang = lang
self.count = count
self.keyword = keyword
self.since = since
self.until = until
# ตรงนี้กะว่าจะเอาไว้ตรวจสอบคำแต่พอทำไปทำมา method go_twitter ทำได้ดีกว่า
check = self.check_keyword(keyword)
self.array_sentiment = []
self.array_sentiment_twi = []
self.array_sentiment_web = []
a = time.time()
# Go search twitter
self.go_twitter(lang, count, keyword, since, until)
print(print("Time: ", time.time() - a))
print("=" * 10 + "End Find Twitter" + "=" * 10)
# bring file that main_twitter has wrote before to cut text by NLP and sorting word for show on GUI
self.cut_text("Twitter", keyword, ["time", "content", "places"], lang,
since, until)
print("Time: ", time.time() - a)
# run webcrawler
def run_webcrawler(self, lang, keyword, since, until):
keyword = keyword.lower()
self.lang = lang
self.keyword = keyword
self.since = since
self.until = until
# ตรงนี้กะว่าจะเอาไว้ตรวจสอบคำแต่พอทำไปทำมา method go_twitter ทำได้ดีกว่า
check = self.check_keyword(keyword)
self.array_sentiment = []
self.array_sentiment_twi = []
self.array_sentiment_web = []
# run search crawler
a = time.time()
self.main_crawler(keyword, lang, since, until)
print(print("Time: ", time.time() - a))
# bring file that main_crawler has wrote before to cut text by NLP and sorting word for show on GUI
self.cut_text("WebCrawler", keyword,
["time", "header", "content", "link"], lang, since,
until)
print("Time: ", time.time() - a)
# find twitter แบบ update ขัอมูลเองได้
def go_twitter(self, lang, count, keyword, since, until):
try:
print("=" * 10 + "Start Find Twitter" + "=" * 10)
read_data = ManageFile("Twitter", keyword + "_Ncut" + lang,
["time", "content", "places"], "r")
csv_data = read_data.managefile_main()
df_in = pandas.DataFrame(csv_data)
# มีของวันไหนบ้าง
condition1 = (df_in[0] >= f"{since} 00:00:00")
condition2 = (df_in[0] <= f"{until} 23:59:59")
temp = [] # temp เก็บวันที่มีในไฟล์นั้นๆ
df_out = df_in[0][condition1 & condition2].str.split(" ").apply(
lambda x: temp.append(x[0]) if x[0] not in temp else None)
for i in range(len(temp)):
temp[i] = datetime.strptime(str(temp[i]), "%Y-%m-%d")
temp.sort(reverse=True)
# -------------------- set since and until time -----------------------
now = datetime.now()
past = now - timedelta(days=7)
now = datetime.strptime(str(now).split(" ")[0], "%Y-%m-%d")
past = datetime.strptime(str(past).split(" ")[0], "%Y-%m-%d")
until_new = until
since_new = since
temp_until = datetime.strptime(until_new, "%Y-%m-%d")
temp_since = datetime.strptime(since_new, "%Y-%m-%d")
if (temp_until >= temp_since):
# set until date
if (temp_until > now and temp_since > now):
return None
else:
if (now > temp_until):
until_new = until_new
else:
until_new = str(now).split(" ")[0]
# set since date
if (temp_until < past and temp_since < past):
return None
else:
if (past < temp_since):
since_new = since_new
else:
since_new = str(past).split(" ")[0]
else:
return None
# ---------------------------------------------------------------------
# --------------------- if can't find data ------------------
if (temp == []):
#print(since_new, until_new, "DO IT",3)
print(since_new, until_new, "DO IT")
self.main_twitter(lang, count, keyword, since_new, until_new)
return None
# --------------------------------------------------------
######################### only Time period that programe can search #############################
new_array = []
end = None
for k in temp:
if (k <= now and k >= now - timedelta(days=7)):
new_array.append(k)
#print(new_array,4)
##################################################################################################
# -------------------------------- find starting time -------------------
point = None
if (datetime.strptime(until_new, "%Y-%m-%d") not in new_array):
# บวก 1 วันเป็นช่วงอ้างอิงให้หาวันเมื่อวาน
point = datetime.strptime(until_new,
"%Y-%m-%d") + timedelta(days=1)
else:
point = datetime.strptime(until_new, "%Y-%m-%d")
point = point.strftime("%Y-%m-%d")
point = datetime.strptime(point, "%Y-%m-%d")
#print(point,5)
# -----------------------------------------------------------------------
# ------------------------------- find ending time ---------------------
if (since_new not in new_array):
# กลับไปวันนึงคือการเอาวันพรุ่งนี้
end = datetime.strptime(since_new,
"%Y-%m-%d") - timedelta(days=1)
new_array.append(end)
#print(new_array,6)
# ----------------------------------------------------------------------
# ------------------------ find specific time --------------------------
for point_stop in new_array:
start = point - timedelta(days=1)
stop = point_stop + timedelta(days=1)
if (start >= stop):
start = str(start).split(" ")[0]
stop = str(stop).split(" ")[0]
print(start, stop, "DO IT")
self.main_twitter(lang, count, keyword, stop, start)
else:
print(start, stop, "DO NOT DO IT")
point = point_stop
# ----------------------------------------------------------------------
except IndexError:
pass
def update_program(self, lang, count, keyword, since, until):
keyword = keyword.lower()
now = datetime.now()
date_since = datetime.strptime(str(since), "%Y-%m-%d")
date_until = datetime.strptime(str(until), "%Y-%m-%d")
since_t = ""
until_t = ""
if (date_since < now - timedelta(days=7)):
since_t = str(now - timedelta(days=7)).split(" ")[0]
else:
since_t = since
if (date_until > now):
until_t = str(now).split(" ")[0]
else:
until_t = until
self.main_twitter(lang, count, keyword, since_t, until_t)
self.main_crawler(keyword, lang, since, until)
self.cut_text("WebCrawler", keyword,
["time", "header", "content", "link"], lang, since,
until)
self.cut_text("Twitter", keyword, ["time", "content", "places"], lang,
since, until)
def update_program_twitter(self, lang, count, keyword):
keyword = keyword.lower()
until = datetime.now()
since = until - timedelta(days=7)
self.main_twitter(lang, count, keyword,
str(since).split(" ")[0],
str(until).split(" ")[0])
def update_program_crawler(self, lang, keyword):
keyword = keyword.lower()
self.main_crawler(keyword, lang, "", "", True)
def cut_text(self, folder, keyword, column, lang, since, until):
# -----------------------read file for content-----------------------
# เอาไฟล์ที่เลือกเวลาแล้วมาตัวคำ
read = None
if (folder == "WebCrawler"):
read = ManageFile(folder, keyword + "_cut" + lang, column, "r")
elif (folder == "Twitter"):
read_data = ManageFile(folder, keyword + "_Ncut" + lang, column,
"r")
# -----------------------อ่านไฟล์เป็น pandas-----------------------
csv_data = read_data.managefile_main()
pd_data = pandas.DataFrame(csv_data)
# --------------------------------------------------------------
# -----------------------เลือกเวลา-----------------------
data_ = self.read_time(folder, pd_data, since, until)
# -----------------------------------------------------
# -----------------------เขียนไฟล์ชั่วคราว-----------------------
data_str = data_.to_csv(index=False)
#print(data_str)
write_file = open(read_data.path + "\\" + keyword + "_cut" + lang +
".csv",
"w",
newline="")
write_file.write(data_str)
write_file.close()
# -----------------------------------------------------------
read = ManageFile(folder, keyword + "_cut" + lang, column, "r")
else:
read = ManageFile(folder, keyword + "_cut" + lang, column, "r")
data = read.managefile_main()
write_sort_text = ManageFile(
"GUI_show", keyword + "_ranking_" + str(folder).lower() + lang,
["keyword", "number"], "w")
write_sort_text_all = ManageFile("GUI_show",
keyword + "_ranking_all" + lang,
["keyword", "number"], "w")
# -------------------------------------------------------------------
# ------------------------------column-------------------------------
column_section = 0
if (folder == "WebCrawler"):
column_section = 2
elif (folder == "Twitter"):
column_section = 1
# -------------------------------------------------------------------
print(
"*****************************************" + folder +
" Start SENTIMENT & NLP*****************************************")
sort_dict = Counter()
first = 0
start = time.time()
for i in data:
# (1) cut text by nlp and do sentiment in the same time
if (first > 0):
cut1 = self.nlp_main.main_nlp(i[column_section])
if (folder == "WebCrawler"):
self.array_sentiment_web.append(
self.sentiment_text(cut1, i[column_section], lang))
elif (folder == "Twitter"):
self.array_sentiment_twi.append(
self.sentiment_text(cut1, i[column_section], lang))
self.array_sentiment.append(
self.sentiment_text(cut1, i[column_section], lang))
print(len(self.array_sentiment))
sort_dict += Counter(cut1)
first += 1
print(
first,
time.time() - start,
"*****************************************" + folder +
" END SENTIMENT & NLP*****************************************")
print("ALL: " + str(len(self.array_sentiment)) + ", Twitter:" +
str(len(self.array_sentiment_twi)) + ", WebCrawler:" +
str(len(self.array_sentiment_web)))
# (2) sort word and write file that can use for show in GUI
for w in sorted(sort_dict, key=sort_dict.get, reverse=True)[:11]:
if (w.lower() != keyword):
write_sort_text.managefile_main([w, sort_dict[w]])
write_sort_text_all.managefile_main([w, sort_dict[w]])
#write_sort_text.close()
#write_sort_text_all.close()
#os.remove(read_data.path+"\\"+keyword+"_cut"+lang+".csv")
#os.remove('E:/Work/Documents/term2/main_group/WebCrawler/valentine_cuten.csv')
def check_keyword(self, keyword):
file_name = 'list_keywords.csv'
file_name_Ncsv = 'list_keywords'
try:
save = open(file_name, "r")
df = pandas.read_csv(save) # check that this lists has got search.
condition = (df["keywords"] == keyword
) # check that this lists has got search.
num = len(df[condition])
# if num > 0 is True it's mean that keyword has got search already.
twitter = ManageFile("Twitter", keyword + "_Ncut" + self.lang,
["time", "content", "places"], "r")
crawler = ManageFile("WebCrawler", keyword + "_Ncut" + self.lang,
["time", "header", "content", "link"], "r")
if (num > 0 and (not twitter.do_it or not crawler.do_it)):
return True
twitter.close()
crawler.close()
save.close()
save = ManageFile("", file_name_Ncsv, ["keywords"], "a")
save.managefile_main([keyword])
except FileNotFoundError:
# first time to run.
temp = open(file_name, "w", newline='')
temp.write("keywords\n")
temp.write(f"{keyword}\n")
temp.close()
return False
def ranking(self, keyword, lang):
# return dataframe ranking word in twitter and crawler
read_data = ManageFile("GUI_show", keyword + "_ranking_all" + lang,
["keyword", "number"], "r")
reader_path = read_data.path + "\\" + keyword + "_ranking_all" + lang + ".csv"
df = pandas.read_csv(reader_path)
return df
def ranking_twitter(self, keyword, lang):
# return dataframe ranking word in twitter
read_data = ManageFile("GUI_show", keyword + "_ranking_twitter" + lang,
["keyword", "number"], "r")
reader_path = read_data.path + "\\" + keyword + "_ranking_twitter" + lang + ".csv"
df = pandas.read_csv(reader_path)
return df
def ranking_webcrawler(self, keyword, lang):
# return dataframe ranking word in crawler
read_data = ManageFile("GUI_show",
keyword + "_ranking_webcrawler" + lang,
["keyword", "number"], "r")
reader_path = read_data.path + "\\" + keyword + "_ranking_webcrawler" + lang + ".csv"
df = pandas.read_csv(reader_path)
return df
def hit_trends_show(self):
self.hit_trends()
# return dataframe hit trends twitter
writer = ManageFile("Hit_Trends", "Hit_Trends", [], "r")
reader_path = writer.path + "\\" + "Hit_Trends.csv"
df = pandas.read_csv(reader_path)
return df
def read_time(self, folder, df_in, since, until):
# return dataframe time
try:
condition1 = (df_in[0] >= f"{since} 00:00:00")
condition2 = (df_in[0] <= f"{until} 23:59:59")
df_out = df_in[condition1 & condition2]
except KeyError:
df_out = df_in
return df_out
def ranking_domain(self, keyword, lang):
# return dataframe domain that exist keyword
read_data = ManageFile("WebCrawler", keyword + "_cut" + lang, [], "r")
df = pandas.DataFrame(read_data.managefile_main())
csv_ = ""
array = []
temp = df[3].str.lower()
if (lang == "en"):
csv_ = df[temp.str.contains("|".join(self.DOMAIN_en))]
array = self.DOMAIN_en
elif (lang == "th"):
csv_ = df[temp.str.contains("|".join(self.DOMAIN_th))]
array = self.DOMAIN_th
elif (lang == "all"):
csv_ = df[temp.str.contains("|".join(self.DOMAIN))]
array = self.DOMAIN
write = ManageFile("Hit_Trends", "top_domain", ["keyword", "number"],
"w")
# sorting domain by number of link has that keyword
sorted_dict = {}
for i in array:
sorted_dict[i] = temp.str.contains(i).sum()
a = {}
for i in sorted(sorted_dict, key=sorted_dict.get, reverse=True):
write.managefile_main([i, sorted_dict[i]])
a[i] = sorted_dict[i]
df_out = pandas.DataFrame({"keyword": a.keys(), "number": a.values()})
return df_out
def sentiment_text(self, dataTh, dataEn, lang):
# return sentiment type pos,neg and neu
if (lang == "en"):
return self.nlp_main.sentiment_eng(dataEn)
elif (lang == "th"):
return self.nlp_main.sentiment_thai(dataTh)
elif (lang == "all"):
temp = self.nlp_main.detection_lang(dataEn)
if (temp == "en"):
return self.nlp_main.sentiment_eng(dataEn)
elif (temp == "th"):
return self.nlp_main.sentiment_thai(dataTh)
def sentiment_show(self):
# return dataframe sentiment twitter and crawler for show on GUI
out = Counter(self.array_sentiment)
df_out = pandas.DataFrame({
"sentiment": out.keys(),
"number": out.values()
})
return df_out
def sentiment_show_twi(self):
# return dataframe sentiment twitter for show on GUI
out = Counter(self.array_sentiment_twi)
df_out = pandas.DataFrame({
"sentiment": out.keys(),
"number": out.values()
})
return df_out
def sentiment_show_web(self):
# return dataframe sentiment crawler for show on GUI
out = Counter(self.array_sentiment_web)
df_out = pandas.DataFrame({
"sentiment": out.keys(),
"number": out.values()
})
return df_out
def stock(self, stock, start, end):
# return dataframe date and adj close price
array_x = []
array_y = []
path = ManageFile("GUI_show").path
try:
# write data from yahoo
ptt = pdr.get_data_yahoo(stock, start=start, end=end)
writer = open(path + "\\" + "stock.csv", "w", newline="")
writer.write(ptt.to_csv())
writer.close()
except:
print("Error:", sys.exc_info()[0])
print("Description:", sys.exc_info()[1])
# read data from file
df = pandas.read_csv(path + "\\" + "stock.csv")
return (df["Date"], df["Adj Close"])
def geometry_map(self, keyword, lang):
# ------------------------------------Read file------------------------------------
read = ManageFile("Twitter", keyword + "_cut" + lang,
["time", "content", "places"], "r")
read_2 = ManageFile("GUI_show", "location_lati_long",
["places", "lati", "long"], "r")
read_3 = ManageFile("GUI_show", "non_exist_lati_long",
["places", "lati", "long"], "r")
# ---------------------------------------------------------------------------------
# ------------------------------------write file------------------------------------
write_exist = ManageFile("GUI_show", "location_lati_long",
["places", "lati", "long"], "a")
write_non_exist = ManageFile("GUI_show", "non_exist_lati_long",
["places", "lati", "long"], "a")
write_data_show = ManageFile("GUI_show", "map_lati_long",
["places", "lati", "long"], "w")
# ---------------------------------------------------------------------------------
location = "" # variable that use for checking that location is exist in database location
lati = ""
longi = ""
# temp array variable that use for checking that location is exist in array location
# use temp array becouse file sometime isn't wrote finished yet
# data is location name that exist & non_exist is location name that nonexistent
non_exist = []
data = []
# data form database
data_exist = []
data_non_exist = []
count = 0 # Counter number of address that new and old
# data_exist is location name that geopy can search
first = 0
for i in read_2.managefile_main():
i[0] = self.nlp_main.clear_name_places(i[0]).lower()
if (first > 0):
data_exist.append(i)
first += 1
first = 0
# data_exist is location name that geopy can't search
for i in read_3.managefile_main():
i[0] = self.nlp_main.clear_name_places(i[0]).lower()
if (first > 0):
data_non_exist.append(i)
first += 1
# read data form twitter database for find latitude & longitude by geopy
first = 0
for i in read.managefile_main():
i[2] = self.nlp_main.clear_name_places(i[2]).lower()
if (first > 0):
if (i[2] == ""):
first += 1
continue
try:
for j in data_exist: # is it exist in DataBase (file)?
if (i[2] == j[0]):
location = "exist1"
lati = j[1]
longi = j[2]
write_data_show.managefile_main(
[i[2], str(lati), str(longi)])
data.append([i[2], str(lati), str(longi)])
print("exist1")
if (location !=
"exist1"): # is it exist in DataBase (temp array)?
for k in data:
if (i[2] == k[0]):
location = "exist2"
lati = k[1]
longi = k[2]
print("exist2")
if (location == "exist2"):
write_data_show.managefile_main(
[i[2], str(lati), str(longi)])
data.append([i[2], str(lati), str(longi)])
if (location != "exist1" and location !=
"exist2"): # it's non_exist in DataBase.
for p in data_non_exist: # is it can use Geopy without error by data in file?
if (i[2] == p[0]):
location = "non_exist"
print("non_exist")
if (i[2] in non_exist):
first += 1
continue
if (location != "non_exist"): # is it a new address?
location2 = self.geolocator.geocode(i[2])
lati = location2.latitude
longi = location2.longitude
print("Geopy")
write_data_show.managefile_main(
[i[2], str(lati), str(longi)])
write_exist.managefile_main(
[i[2], str(lati), str(longi)])
data.append([i[2], str(lati), str(longi)])
count += 1
location = ""
except AttributeError:
write_non_exist.managefile_main(
[i[2], str(lati), str(longi)])
non_exist.append(i[2])
except geopy.exc.GeocoderUnavailable:
write_non_exist.managefile_main(
[i[2], str(lati), str(longi)])
non_exist.append(i[2])
except geopy.exc.GeocoderServiceError:
pass
first += 1
print(len(data), count) # counting location & counting using geopy
# ----------------return dataframe location by latitude & longitude & name places----------------
dict_ = {}
places_array = []
lati_array = []
long_array = []
for i in data:
places_array.append(i[0])
lati_array.append(i[1])
long_array.append(i[2])
dict_["places"] = places_array
dict_["lati"] = lati_array
dict_["long"] = long_array
if (data == []):
dict_ = {"places": [], "lati": [], "long": []}
df = pandas.DataFrame(dict_)
# ------------------------------------------------------------------------------------------------
return df
def setUp(self):
csv.field_size_limit(256 << 20)
self.nlp = NLP()
self.websites_crawler = websites_crawler(self.nlp)
self.websites_crawler.count = 1
self.start = time.time()
def setUp(self):
self.nlp = NLP()
self.start = time.time()
class Test(unittest.TestCase):
def setUp(self):
self.nlp = NLP()
self.start = time.time()
# **********************************************NLP**********************************************
def test_main_nlp(self):
print("*****test_main_nlp*****")
datas = "ตอนนี้อาจารย์กำลังรอให้เพื่อนคุณคนนึง ส่งเมล์ยืนยันกลับมาว่าทราบเรื่องการสอบ และส่ง Microsoft Account มาให้อจ. เพิ่มเข้าไปในผู้เข้าสอบ (อจ. จะเพิ่มเป็นไฟล์ ทำทีเดียวเลย น่าจะทำการเพิ่มได้เสร็จภายในวันนี้) - ข้อสอบ เป็นแบบให้พิมพ์คำตอบด้วยคีย์บอร์ด มีทั้งหมด 30 ข้อ 160 คะแนน โดยแต่ละข้อจะบอกคะแนนกำกับไว้แล้ว"
made = self.nlp.main_nlp(datas)
in_put = type(made)
#mode = "equal"
condition = type(list())
print(f'{in_put}, {condition} OK')
self.assertEqual(in_put, condition)
print("*****test_main_nlp*****")
def test_detection_lang(self):
print("*****test_detection_lang*****")
datas = "ตอนนี้อาจารย์กำลังรอให้เพื่อนคุณคนนึง ส่งเมล์ยืนยันกลับมาว่าทราบเรื่องการสอบ และส่งMicrosoft Account มาให้อจ. เพิ่มเข้าไปในผู้เข้าสอบ (อจ. จะเพิ่มเป็นไฟล์ ทำทีเดียวเลย น่าจะทำการเพิ่มได้เสร็จภายในวันนี้) - ข้อสอบ เป็นแบบให้พิมพ์คำตอบด้วยคีย์บอร์ด มีทั้งหมด 30 ข้อ 160 คะแนน โดยแต่ละข้อจะบอกคะแนนกำกับไว้แล้ว"
lang = "th"
made = self.nlp.detection_lang(datas)
in_put = made
#mode = "equal"
condition = lang
print(f'{in_put}, {condition} OK')
self.assertEqual(in_put, condition)
print("*****test_detection_lang*****")
def test_sentiment_eng(self):
print("*****test_sentiment_eng*****")
datas = "The power of modern search engines is undeniable: you can summon knowledge from the internet at a moment’s notice. Unfortunately this superpower isn’t omnipresent. There are many situations where search is relegated to strict keyword search or when the objects aren’t text search may not be available. Furthermore strict keyword search doesn’t allow the user to search semantically which means information is not as discoverable."
mode = "neu"
in_put = self.nlp.sentiment_eng(datas)
#mode2 = "equal"
condition = mode
print(f'{in_put}, {condition} OK')
self.assertEqual(in_put, condition)
print("*****test_sentiment_eng*****")
def test_sentiment_thai(self):
print("*****test_sentiment_thai*****")
datas = "ตอนนี้อาจารย์กำลังรอให้เพื่อนคุณคนนึง ส่งเมล์ยืนยันกลับมาว่าทราบเรื่องการสอบ และส่งMicrosoft Account มาให้อจ. เพิ่มเข้าไปในผู้เข้าสอบ (อจ. จะเพิ่มเป็นไฟล์ ทำทีเดียวเลย น่าจะทำการเพิ่มได้เสร็จภายในวันนี้) - ข้อสอบ เป็นแบบให้พิมพ์คำตอบด้วยคีย์บอร์ด มีทั้งหมด 30 ข้อ 160 คะแนน โดยแต่ละข้อจะบอกคะแนนกำกับไว้แล้ว"
mode = "neu"
result = self.nlp.main_nlp(datas)
temp = self.nlp.sentiment_thai(result)
in_put = temp
#mode = "equal"
condition = mode
print(f'{in_put}, {condition} OK')
self.assertEqual(in_put, condition)
print("*****test_sentiment_thai*****")
def test_loadData_and_storeData(self):
print("*****test_loadData_and_storeData*****")
datas = [123]
self.nlp.storeData(datas, "ลบออก")
in_put = self.nlp.loadData("ลบออก")
#mode = "equal"
condition = datas
print(f'{in_put}, {condition} OK')
self.assertEqual(in_put, condition)
print("*****test_loadData_and_storeData*****")
def test_clear_name_places(self):
print("*****test_clear_name_places*****")
#pattern = [str(i) for i in range(10)]
datas = "🇫🇷 Bastille Day 🎂 Birthday 🛍️ Black Friday ✊🏿 Black Lives Matter 🇨🇦 Canada Day 🇧🇷 Carnaval 🐉 Chinese New Year 🎅 Christmas🇲🇽 Cinco de Mayo 🦠 Coronavirus"
result = self.nlp.clear_name_places(datas)
in_put = emoji.get_emoji_regexp().search(result)
#mode = "equal"
condition = None
print(f'{in_put}, {condition} OK')
self.assertEqual(in_put, condition)
print("*****test_clear_name_places*****")