def tag_article():
"""
주기 : 하루
작업 : 게시물의 명사를 기준으로 테그한다.
"""
from konlpy.tag import Hannanum
hannanum = Hannanum()
for article in Article.objects.all():
try:
tags = [
tag for tag in hannanum.nouns(article.title) if len(tag) > 1
]
for tag in tags[:10]:
splits = re.split(r',', tag)
tags.remove(tag)
if len(splits) > 1:
for split in splits:
tags.append(split.strip(SPLIT_CHAR))
else:
tags.append(tag.strip(SPLIT_CHAR))
article.do_tag(tags)
except Exception as e:
print(hannanum.nouns(article.title))
print(e)
def max_similarity(self):
konlpy = Hannanum()
l = konlpy.nouns(self.lyrics_input)
song_list = self.song_list
song_id = 0
max_similarity = 0.0
result = self.compare_lyrics()
if result > 0:
return result
print("입력된 가사의 단어 배열: ", l)
for song in song_list:
if song['words'] is None:
song['words'] = konlpy.nouns(song['lyrics'])
print("song_id, title: ", song['song_id'], song['title'])
temp = self.measure_similarity(l, song['words'])
print("코사인 유사도: ", temp)
print()
if temp > max_similarity:
song_id = song['song_id']
# title 출력을 원한다면 주석해제
# title = song['title']
max_similarity = temp
# title 출력을 원한다면 주석해제
return song_id # , title
def lineAnalyzer(sentence, analyzeType):
hannanum = Hannanum()
wordList = list()
if (analyzeType == 1):
# Nouns
wordList = hannanum.nouns(str(sentence))
elif (analyzeType == 2):
# Morphs
wordList = hannanum.morphs(str(sentence))
elif (analyzeType == 3):
# Bi-grams
bigram_measures = collocations.BigramAssocMeasures()
pos = hannanum.pos(str(sentence))
words = [s for s, t in pos]
finder = collocations.BigramCollocationFinder.from_words(words)
finder.apply_word_filter(lambda w: len(w) < 2)
finder.apply_freq_filter(3)
wordList = finder.nbest(bigram_measures.pmi, 10)
elif (analyzeType == 4):
# Tri-grams
trigram_measures = collocations.TrigramAssocMeasures()
pos = hannanum.pos(str(sentence))
words = [s for s, t in pos]
finder = collocations.TrigramCollocationFinder.from_words(words)
finder.apply_word_filter(lambda w: len(w) < 2)
finder.apply_freq_filter(3)
wordList = finder.nbest(trigram_measures.pmi, 10)
else:
print("error on top!")
return wordList
def generate_wordCloud(text, font_path, extractNum = 15):
hannanum = Hannanum()
setFont(font_path)
## mask image
image_mask = np.array(Image.open("./utils/visualize/만세_보노.jpg"))
cleanText = clean_text(text)
words = hannanum.nouns(cleanText)
word_list = flatten(words)
word_list = pd.Series([x for x in word_list if len(x)>1]) #; print( word_list.value_counts().head(20) )
stopwordList = ['’','”','‘','·','…','"',"'"]
wordcloud = WordCloud(font_path=font_path
, stopwords=stopwordList
, width=800, height=800
, mask=image_mask
, background_color='white')
count = Counter(word_list)
wordcloud = wordcloud.generate_from_frequencies(count)
array = wordcloud.to_array()
fig = plt.figure(figsize=(10,10))
plt.imshow(array, interpolation='bilinear')
plt.axis("off")
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
string = b64encode(buf.read())
wcURI = 'data:image/png;base64,' + urllib.parse.quote(string)
count = count.most_common(extractNum)
barURI = generate_barchart(count)
return wcURI, barURI, count
def generate_summary(self, file_name, index, top_n=5):
stop_words = read_data(filename='korean_stopwords_list.txt')
summarize_text = []
# Step 1 - Read text anc split it
sentences = self.read_article(file_name, index)
#token화 추가
hannanum = Hannanum()
temp = []
for sentence in sentences:
temp.append(hannanum.nouns(' '.join(sentence)))
# print("temp:",temp)
# Step 2 - Generate Similary Martix across sentences
sentence_similarity_martix = self.build_similarity_matrix(
temp, stop_words)
# Step 3 - Rank sentences in similarity martix
sentence_similarity_graph = nx.from_numpy_array(
sentence_similarity_martix)
scores = nx.pagerank(sentence_similarity_graph)
# Step 4 - Sort the rank and pick top sentences
ranked_sentence = sorted(
((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
for i in range(top_n):
summarize_text.append(" ".join(ranked_sentence[i][1]))
# Step 5 - Offcourse, output the summarize text
print("\nSummarize Text: \n", ". ".join(summarize_text))
def get_string(path):
f = open(path, "r", encoding="utf-8")
sample = f.read()
f.close()
h = Hannanum()
list_nouns = h.nouns(sample) #get list of nouns from sample
return listToString(list_nouns) #get string of list_nouns
def get_tags(text, ntags=50, multiplier=10):
h = Hannanum()
nouns = h.nouns(text)
count = Counter(nouns)
print(count)
return [{'color': color(), 'tag': n, 'size': c * multiplier} \
for n, c in count.most_common(ntags)]
def test():
setFont()
hannanum = Hannanum()
#DB Connecion
# conn = oci.connect("test/[email protected]:32764/xe", charset='utf8')
conn = oci.connect('test','1234','192.168.0.52:32764/xe', encoding='utf-8')
df = pd.read_sql('select * from article_sample', conn )
sample1 = df['ARTICLE_CONTENT'][0].read()
word = hannanum.nouns(sample1)
word_list = flatten(word)
word_list = pd.Series([x for x in word_list if len(x)>1])
print( word_list.value_counts().head(20) )
stopwordList = ''
wordcloud = WordCloud(font_path=setFontPath()
, stopwords=stopwordList
, width=800, height=800
, background_color='white')
count = Counter(word_list)
wordcloud = wordcloud.generate_from_frequencies(count)
array = wordcloud.to_array()
fig = plt.figure(figsize=(10,10))
plt.imshow(array, interpolation='bilinear')
plt.axis("off")
plt.show()
# plt.savefig('C:/Users/admin/Documents/IMG04.png', bbox_inches='tight')
def text_preprocessing_after(lists):
hannanum = Hannanum()
getNum = 5
stopword = ['등', '코', '만', '속보', '최초', '4억', '월요일']
cleaning = lambda x: hannanum.nouns(wordcloud01.clean_text(x))
nouns_list = list(map(cleaning, lists))
# print(nouns_list)
texts = [value for nouns in nouns_list for value in nouns]
total_counter = Counter(texts)
for word in stopword:
del total_counter[word]
result = total_counter.most_common(getNum)
return result
## 명사 빈도 추출. ##################################################
# def nouns_frequency(text):
# print('Kkma 객체 생성')
# hannanum = Kkma()
# print('텍스트 처리중')
# clean_text = wordcloud01.clean_text(text)
# print('텍스트 명사 처리중')
# words = hannanum.nouns(clean_text)
# print('평평하게 만들기')
# word_list = wordcloud01.flatten(words)
# print('판다스 변환중')
# word_list = pd.Series([x for x in word_list if len(x)>1])
# print('result Counter 중')
# result = Counter(word_list)
# return result
def test():
rss_list = [
# "https://www.reddit.com/",
"http://www.chosun.com/site/data/rss/politics.xml",
"http://rss.joins.com/joins_politics_list.xml",
]
hannanum = Hannanum()
# mecab = Macab()
for rss_link in rss_list:
print("Start get_URLs and read files from : " + rss_link)
start_time = time.time()
links = get_URLs(rss_link)
for link in links:
parse_time = time.time()
article = get_article(link)
file = open("./test/%s.txt" % (article.title),
'w',
encoding="utf8")
nouns = hannanum.nouns(article.text)
# nouns = mecab.nouns(article.text)
for noun in nouns:
file.write("%s\n" % noun)
file.close()
parse_time = time.time() - parse_time
print("parse files from %s: %f" % (link, parse_time))
start_time = time.time() - start_time
print("Process time : %f" % (start_time))
def parse(df, _type: str):
"""Parse function"""
# Parser
korean_parser = Hannanum()
neg = df[df['label'] == 0]['document'].tolist()
pos = df[df['label'] == 1]['document'].tolist()
nouns_doc_f = open('./input/nouns_{}_documents.txt'.format(_type), 'w')
nouns_label_f = open('./input/nouns_{}_labels.txt'.format(_type), 'w')
morphs_doc_f = open('./input/morphs_{}_documents.txt'.format(_type), 'w')
morphs_label_f = open('./input/morphs_{}_labels.txt'.format(_type), 'w')
logger.info("Starting parsing...")
for doc in neg:
try:
nouns_doc_f.write(','.join(korean_parser.nouns(doc)) + '\n')
nouns_label_f.write('{}\n'.format(0))
except:
pass
try:
morphs_doc_f.write(','.join(korean_parser.morphs(doc)) + '\n')
morphs_label_f.write('{}\n'.format(0))
except:
pass
logger.info('%s pos document parsing completed.' % _type)
for doc in pos:
try:
nouns_doc_f.write(','.join(korean_parser.nouns(doc)) + '\n')
nouns_label_f.write('{}\n'.format(1))
except:
pass
try:
morphs_doc_f.write(','.join(korean_parser.morphs(doc)) + '\n')
morphs_label_f.write('{}\n'.format(1))
except:
pass
logger.info('%s neg document parsing completed.' % _type)
nouns_doc_f.close()
nouns_label_f.close()
morphs_doc_f.close()
morphs_label_f.close()
def get_tags(text, ntags=50, multiplier=10): h = Hannanum() nouns = h.nouns(text) count = Counter(nouns) # for word,cnt in count.most_common(ntags): # print(word,cnt) return count
def review_preprocessing(data):
# Hannanum package
pos_tagger = Hannanum()
# 뉴스를 tokenizing한 후, 명사만 추출
pos_nouns = pos_tagger.nouns(data)
return ' '.join(pos_nouns)
def text_mining(title_list, ntags=50, multiplier=1):
h = Hannanum()
data_nouns = []
for title in title_list:
data_nouns.extend(h.nouns(title))
count = Counter(data_nouns)
return [{'color': color(),'tag':n,'size':int(c*multiplier*0.5)} for n,c in count.most_common(ntags)]
def comment_freq(youtube_data):
# youtuber_csv_data = dm.GetData(url, con)
# if youtuber_csv_data == None:
# print("데이터 없음")
# return None
# video_num = int(input("몇 번 동영상을 분석할까요 ? "))
# youtube_data = dm.GetData(youtuber_csv_data[video_num][0], password) >> main.py에서 구현
if youtube_data == None:
return None
comment = []
for i in range(len(youtube_data)):
comment.append(youtube_data[i][2])
emoji_pattern = re.compile(
"["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
"]+",
flags=re.UNICODE)
han = re.compile(r'[ㄱ-ㅎㅏ-ㅣ!?~,".\n\r#\ufeff\u200d]')
comment_noemot = []
for i in comment:
tokens = re.sub(emoji_pattern, "", i)
tokens = re.sub(han, "", tokens)
comment_noemot.append(tokens)
nouns = []
h = Hannanum()
for i in comment_noemot:
n = h.nouns(i)
nouns.append(n)
noun_list = []
for i in range(len(nouns)):
for j in range(len(nouns[i])):
noun_list.append(nouns[i][j])
counts = Counter(noun_list)
tags = counts.most_common(30)
wc = WordCloud(font_path='C:\\Windows\\Fonts\\gulim.ttc',
background_color='black',
width=800,
height=600)
cloud = wc.generate_from_frequencies(dict(tags))
cloud
plt.figure(figsize=(10, 8))
plt.axis('off')
plt.imshow(cloud)
plt.show()
def tokenization(cleaned_docs):
han = Hannanum()
tokenized_docs = []
while ' ' in cleaned_docs:
cleaned_docs.remove(' ')
for doc in cleaned_docs:
nouns_in_doc = []
for noun in han.nouns(doc):
if len(noun) > 1: nouns_in_doc.append(noun)
tokenized_docs.append(nouns_in_doc)
return tokenized_docs
def update_words_all():
hannanum = Hannanum()
db = db_connector.DbConnector()
song_list = db.select_all()
for song in song_list:
if song['lyrics'] is not None and song['words'] is None:
words = hannanum.nouns(song['lyrics'])
words = sorted(set(words))
update_words(song['song_id'], ' '.join(words))
print('Words extraction done!')
def wordAnalysis(text):
myHannanum = Hannanum()
print("text : " + text)
replace_text = re.sub("[!@#$%^&*()_+]", " ", text)
print("replace_text : " + replace_text)
analysis_text = (" ".join(myHannanum.nouns(replace_text)))
return analysis_text
def reduceToWords(self):
hannanum = Hannanum()
words = ''
#for word in hannanum.nouns(unicode(texts, 'UTF-8')):
if (self.result != ''):
for word in hannanum.nouns(self.result):
word = re.sub("[(*&]", "", word)
if (len(word) > 1): words = word + '\n' + words
#for end
self.result = words
print words
# if end
return self
def extract(self): # extract nouns with Komoran
hnn = Hannanum()
# merge = str(self.merge_sentence.encode('utf-8'), encoding='utf-8') # 인코딩 문제 해결 못함 *
merge = self.merge_sentence
nouns = hnn.nouns(merge)
for n in nouns: # 특수문자 제거
n = n.replace("'", "").replace(",", "")
processed = [n for n in nouns if len(n) >= 2] # min length 2
count = Counter(processed)
self.tags = count.most_common(20) # max character 20
def reduceToWords(self) :
hannanum = Hannanum()
words = ''
#for word in hannanum.nouns(unicode(texts, 'UTF-8')):
if(self.result != '') :
for word in hannanum.nouns(self.result):
word = re.sub("[(*&]", "", word)
if(len(word) > 1): words = word + '\n' + words
#for end
self.result = words
print words
# if end
return self
def crawl():
global hannanum
if hannanum == None:
hannanum = Hannanum()
if jpype.isJVMStarted():
jpype.attachThreadToJVM()
hannanum = Hannanum()
media = Media.objects.all()
articles = Article.objects.all()
count = 0
all = 0
for medium in media:
links = get_URLs(medium.rss_list)
#print(links)
upper_bound = len(links)
all += upper_bound
for link in links:
#print(link)
if Article.objects.filter(article_url=link).exists():
continue
try:
article = get_article(link)
except:
print("Fail:%s" % link)
continue
#print(link)
title = article.title
content = article.text
nouns = hannanum.nouns(article.text)
morphemed_content = " ".join(nouns)
writer = ''
if len(article.authors) == 0:
writer = 'anonymous'
else:
writer = article.authors[0]
try:
articles.create(
title=title,
content=content,
morphemed_content=morphemed_content,
media=medium,
writer=writer,
article_url=link,
)
count += 1
except:
print("Fils:%s,title:%s" % (link, title))
continue
return (count, all)
def insert_summary():
mongoDB = myMongoDB("CapstoneTest")
#fasttext.util.download_model('ko', if_exists='ignore')
ft = fasttext.load_model('./models/cc.ko.300.bin')
total_clean_sentence = []
string_id = []
for content in mongoDB.collected.find({}, {"_id": 1, "content": 1}):
cleaned_sentence = []
clean_sentence = []
string_id.append(list(content.values())[0])
string = list(content.values())[1]
string = string.replace(u'\xa0', u' ')
string = string.replace(u'\n', u' ')
string = string.replace(u'\r', u' ')
clean_sentence.append(sent_tokenize(string))
for i in clean_sentence:
for j in i:
cleaned_sentence.append(j)
total_clean_sentence.append(cleaned_sentence)
temp = []
hannanum = Hannanum()
for clean_sentence in total_clean_sentence:
for s in clean_sentence:
noun = hannanum.nouns(s)
for i in noun:
temp.append(i)
for i in temp:
word_vector_arr = np.asarray(ft[i], dtype='float32')
word_dict[i] = word_vector_arr
string_idx = 0
for clean_sentence in total_clean_sentence:
article_embedding = articles_to_vectors(clean_sentence)
similar_matrix = similarity_matrix(article_embedding)
score = calculate_score(similar_matrix)
summaryShort_list = summaryShort(clean_sentence, score)
summaryMed_list = summaryMed(clean_sentence, score)
summaryLong_list = summaryLong(clean_sentence, score)
mongoDB.collected.update_one({'_id': string_id[string_idx]}, {
'$set': {
'sum_short': summaryShort_list,
'sum_mid': summaryMed_list,
'sum_long': summaryLong_list
}
})
string_idx += 1
def post(
self,
request,
format=None
): #JSON: "key" : "value" --> "searchWord" : "보온보냉팩 버리는 방법 좀 알려줘?"
searchSentence = request.data['searchWord'] #안드로이드에서 searchWord 입력해야함
if "캔" in searchSentence:
print("Okt")
okt = Okt()
Nouns = okt.nouns(searchSentence)
else:
print("Hannanum")
ha = Hannanum()
Nouns = ha.nouns(searchSentence)
print('nouns: ', Nouns)
Idx = []
temp = []
small_list = list()
for word in Nouns:
smallIdx = WasteCategoryS.objects.filter(cg_name__contains=word)
for val in smallIdx:
small_list.append(val)
if len(smallIdx) == 0:
print('len 0')
middleIdx = WasteCategoryM.objects.filter(
cg_name__contains=word)
for ob in middleIdx:
Idx.append(ob.idx)
continue
for ob in smallIdx:
Idx.append(ob.cg_middle_idx.idx)
print(Idx)
dischargeTipsList = []
for idx in Idx:
dischargeTipsList.append(
DischargeTips.objects.get(category_m_idx=idx))
serializer = DischargeTipsSerializer(dischargeTipsList, many=True)
waste_serializer = WasteCategorySSerializer(small_list, many=True)
return Response(
{
"matching_name": waste_serializer.data,
"textVoiceDischargeTips": serializer.data
},
status=status.HTTP_201_CREATED)
def main(args=None):
print("Loading pdf files...")
outfp = extract_text(files=search("./sample_pdf"), outfile="temp.txt")
outfp.close()
print("Loading data...")
f = open("temp.txt", encoding='UTF8')
elements = f.readlines()
elements = [x for x in elements if x != "\n"]
elements = [x.rstrip() for x in elements]
hannanum = Hannanum()
korean_list = []
korean_noun_list = []
english_list = []
korean = re.compile('[^ ㄱ-ㅣ가-힣]+')
for element in elements:
korean_list.append(korean.sub("", element))
korean_list = [x.strip() for x in korean_list]
korean_list = [x for x in korean_list if x != '']
print("Parsing Korean words...")
for korean in korean_list:
korean_noun_list += hannanum.nouns(korean)
korean_noun_list = [x for x in korean_noun_list if len(x) > 1]
print("Korean list : ", korean_noun_list)
print("Parsing English words...")
for element in elements:
english_list.append(re.sub('[^a-zA-Z]', '', element))
english_list = [x.strip() for x in english_list]
english_list = [x for x in english_list if x != '']
print("English list : ", english_list)
korean_counter = collections.Counter(korean_noun_list)
print("Most 10 word in Korean words:", korean_counter.most_common(10))
english_counter = collections.Counter(english_list)
print("Most 10 word in English words:", english_counter.most_common(10))
draw_word_cloud(korean_noun_list, "Korean")
draw_word_cloud(english_list, "English")
print("Done")
def draw_cloud(reviews):
tags = {}
# r = lambda: random.randint(0,255)
# color = lambda: (r(), r(), r())
for review in reviews:
h = Hannanum()
nouns = h.nouns(review)
count = dict(Counter(nouns))
tags = {
k: tags.get(k, 0) + count.get(k, 0)
for k in set(tags) | set(count)
}
gen_stylecloud(text=tags,
output_name="wordcloud.png",
icon_name="fas fa-square-full",
background_color="white",
font_path="Jua-Regular.ttf",
size=1024)
def text_preprocessing(queryset):
# print(queryset,len(queryset))
hannanum = Hannanum()
getNum = 5
stopword = ['등', '코', '만', '속보', '최초', '4억', '월요일']
df = pd.DataFrame.from_records(queryset)
# print(df, type(df))
# df['title_nouns'] = df['article_title'].apply( lambda x : hannanum.nouns( wordcloud01.clean_text( x ) ) ); print(df['title_nouns']); print(df['title_nouns'].sum())
# print('apply시작')
df['title_nouns'] = df['article_title'].apply(
lambda x: Counter(hannanum.nouns(wordcloud01.clean_text(x))))
# print('sum시작')
total_counter = df['title_nouns'].sum()
# print('stopword')
for word in stopword:
del total_counter[word]
# print(type(total_counter), total_counter.most_common( getNum ))
result = total_counter.most_common(getNum)
return result
def rec_hashtag(request):
"""
해시태그 추천 시 사용할 API
---
"""
content = request.data.get('article')
hannanum = Hannanum()
keywords = hannanum.nouns(content)
count = Counter(keywords)
count = sorted(sorted(count.items(), key=operator.itemgetter(0)),
key=lambda x: x[1],
reverse=True)
data = []
for k in count:
data.append(k[0])
if len(data) > 2:
break
return Response(data)
def morp_analysis(self):
noun_text = re.sub('[-_=+,#/\?:^$.@*\"※~&%ㆍ·!』\\‘’|\(\)\[\]\<\>`\'…》]', '', str(self))
noun_text = re.sub('\n', '', noun_text)
hannanum = Hannanum()
text_list = hannanum.nouns(noun_text) # 명사 분석
word_list = pd.Series(text_list)
result = word_list.value_counts().head(10)
result_values = list(result.values)
for i in range(len(result_values)):
result_values[i] = np.int16(result_values[i]).item()
freq_lst = []
for i in range(len(result)):
freq_lst.append({'word': result.keys()[i], 'freq': result_values[i]})
return freq_lst
def get_tags(text, ntags=50, multiplier=2):
h = Hannanum()
nouns = h.nouns(text)
long_nouns = list()
for n in nouns:
if len(n) >= 2: # 길이가 2 이상인 명사만 출력
long_nouns.append(n)
count = Counter(long_nouns)
word_list = list()
for w in count.most_common(ntags):
if w[1] >= 10: # 10번 이상 나온 명사만 출력
print(w)
word_list.append(w)
r = lambda: random.randint(0, 255)
color = lambda: (r(), r(), r())
return [{'color': color(), 'tag': n, 'size': c*multiplier}\
for n, c in word_list]
def wordcloud_textmining(text):
H = Hannanum()
twttierMS = np.array(Image.open('./static/images/동그라미.png'))
a = " ".join(H.nouns(text))
wc = WordCloud(font_path="./font/BMEULJIROTTF.ttf",
background_color="white",
width=1000,
height=1000,
mask=twttierMS,
max_words=150,
max_font_size=200)
wc.generate(a)
fig = plt.figure()
plt.imshow(wc, interpolation='bilinear')
plt.axis('off')
plt.savefig('./static/Wordcloud.png')
return fig
def get_text():
full_data = ""
#폴더내의 모든 파일 가지고 오기
files = glob.glob(file_dir + '/*.json')
corpus = list()
h = Hannanum()
for fname in files:
# print("fname")
print(fname)
file_name = fname.split('\\')[1]
file_idx = file_name.split('.')[0]
print(file_idx) #이게 파일이름이지뭐...
idx_list.append(file_idx) #*json파일을 읽었는데, 읽은 순서를 저장하고 싶으면 이렇게 하면됨
with open(fname, encoding='UTF8') as json_file:
json_data = json.load(json_file)
#key가 app_detail 인 문자열 가지고 오기
json_string = json_data["app_detail"] # 개별문자
# corpus.append(json_string)#=>이걸 사용해서 corpus를 만들면 명사만 엮인게 아니라서 이상한 애들이 많이 들어감
#corpus에 명사만 담기 위함(형태소 자르기)
nouns = h.nouns(json_string)
#list를 string으로 변환
nouns = ' '.join(nouns) #list의 element들을 공백을 이용해서 구분함
# print("nouns 출력")
# print(nouns)
corpus.append(nouns) #corpus에 명사만 담을 것임!
######
app_category = json_data["app_category"]
### print(json_string)
return full_data, app_category, corpus
def preTexts(comments, videoId):
hannanum = Hannanum()
main = pd.Series()
for text in comments:
text = processText(text)
text_list = hannanum.nouns(text)
main = main.append(pd.Series(text_list))
dir = os.path.dirname(os.path.abspath(__file__))
main.to_csv(f'{dir}/data/{videoId}.csv')
result = main.value_counts().to_list()
idx_result = main.value_counts().index.to_list()
loop = 30
if (pd.DataFrame(result).shape[0] < loop):
loop = result.shape[0]
ret = []
for i in range(loop):
ret.append([idx_result[i], result[i]])
#result.to_csv(f'{dir}/data/comm{videoId}.csv')
return ret
def get_tags(self,text, ntags=10, multiplier=10):
h = Hannanum()
nouns = h.nouns(text)
count = Counter(nouns)
return [{'tag': n, 'size': c*multiplier }\
for n, c in count.most_common(ntags)]
def get_tags(text, ntags=50, multiplier=10):
h = Hannanum()
nouns = h.nouns(text)
count = Counter(nouns)
return [{"color": color(), "tag": n, "size": c * multiplier} for n, c in count.most_common(ntags)]
def WordCount(corpus):
h = Hannanum()
nouns = h.nouns(corpus)
frequency = Counter(nouns)
return frequency
def get_tags(text, ntags=int(sys.argv[2]), multiplier=10):
h = Hannanum()
nouns = h.nouns(text)
count = Counter(nouns)
return [{ 'color': color(), 'tag': n, 'size': c*multiplier }\
for n, c in count.most_common(ntags)]
class AnalysisDiction:
"""
This class is for analysis of korean texts using kkma and twitter dictionaries
"""
def __init__(self, on_han=False, on_twitter=False, on_mecab=False): # maybe move to init of analysis_app
"""
Allocate kkma or twitter diction instance
:param on_han: han instance
:param on_twitter: twitter instance
:param on_mecab: mecab instance
"""
if on_han is True:
self.han = Hannanum()
if on_twitter is True:
self.twitter = Twitter()
# if on_mecab is True:
# self.mecab = Mecab()
def analyzer_hannaum(self, string_data, mode):
"""
This method is for hannanum. It acts differently depends on its mode.
:param string_data: String data for analysis
:param mode: Analyze string data depending on its mode
:return: Return its results. If have no mode in param , return false
ref: http://konlpy.org/ko/v0.4.4/api/konlpy.tag/#module-konlpy.tag._hannanum
"""
if mode is 'morphs':
return self.han.morphs(string_data)
elif mode is 'nouns':
return self.han.nouns(string_data)
elif mode is 'pos':
return self.han.pos(string_data)
else:
return False
def analyzer_mecab(self, string_data, mode):
"""
This method is for mecab. It acts differently depends on its mode.
:param string_data: String data for analysis
:param mode: Analyze string data depending on its mode
:return: Return its results. If have no mode in param , return false
ref: http://konlpy.org/ko/v0.4.4/api/konlpy.tag/#mecab-class
"""
if mode is 'morphs':
return self.mecab.morphs(string_data)
elif mode is 'nouns':
return self.mecab.nouns(string_data)
elif mode is 'pos':
return self.mecab.pos(string_data)
else:
return False
def analyzer_twitter(self, string_data, mode):
"""
This method is for twitter. It acts differently depends on its mode.
:param string_data: String data for analysis
:param mode: Analyze string data depending on its mode
:return: Return its results. If have no mode in param , return false
ref: http://konlpy.org/ko/v0.4.4/api/konlpy.tag/#module-konlpy.tag._twitter
"""
if mode is 'morphs':
return self.twitter.morphs(string_data)
elif mode is 'nouns':
return self.twitter.nouns(string_data)
elif mode is 'pos':
return self.twitter.pos(string_data)
elif mode is 'posmore':
return self.twitter.pos(string_data, True, True)
else:
return False
