def completeRake(content):
rakeObject = RakeKeywordExtractor(set([]))
keywordList = rakeObject.extract(content,True)
words = nltk.word_tokenize(content)
content_lemmatized = ""
words = list(map(lambda x: lemma_obj.lemmatize(x),words))
content_lemmatized = ' '.join(words)
content = content_lemmatized
freq = {}
freq_dist = nltk.FreqDist(words)
keyword_freq=getKeywordFrequency(keywordList)
adjacency_freq = getAdjacencyFrequency(keywordList,content,words)
sortedFreqList = sorted(freq_dist.items(), key = lambda x: x[1],reverse=True)
additional_stopwords=[]
for key in sortedFreqList:
keyword_freq.setdefault(key[0],0)
adjacency_freq.setdefault(key[0],0)
if(adjacency_freq[key[0]]>keyword_freq[key[0]]):
additional_stopwords.append(key[0])
additional_stopwords = set(additional_stopwords)
newRakeObject = RakeKeywordExtractor(additional_stopwords)
newKeywordList = newRakeObject.extract(content)
for keywords in newKeywordList:
print keywords
def extractKeywords(phrase_list): RAKE_OBJ = RakeKeywordExtractor(set([])) word_scores = RAKE_OBJ._calculate_word_scores(phrase_list) phrase_scores = RAKE_OBJ._calculate_phrase_scores(phrase_list, word_scores) sorted_phrase_scores = sorted(phrase_scores.iteritems(),key=operator.itemgetter(1), reverse=True) n_phrases = len(sorted_phrase_scores) return sorted_phrase_scores[0:int(n_phrases)]
def extractKeywords(phrase_list): RAKE_OBJ = RakeKeywordExtractor(set([])) word_scores = RAKE_OBJ._calculate_word_scores(phrase_list) phrase_scores = RAKE_OBJ._calculate_phrase_scores(phrase_list, word_scores) sorted_phrase_scores = sorted(phrase_scores.iteritems(),key=operator.itemgetter(1), reverse=True) n_phrases = len(sorted_phrase_scores) return sorted_phrase_scores[0:int(n_phrases/2)]
def main():
# SET N AS THE NUMBER OF KEYWORDS TO EVALUATE FROM THE KEYWORDS LISTS
N = 243
base_dir = os.path.dirname(os.path.realpath(__file__))
stopwords_simple = os.path.join(base_dir,'stopwords_nltk.txt')
rake = RakeKeywordExtractor(stopwords_simple)
tp_total = 0
fp_total = 0
fn_total = 0
corpus = os.path.join(base_dir,'./corpus')
txtfiles = [file for file in os.listdir(corpus) if file.endswith('.txt')]
s=""
for txtfile in txtfiles:
try:
keyfile = os.path.join(corpus,txtfile).replace('.txt', '.key')
content = open(os.path.join(corpus,txtfile), 'r').read().decode('utf-8')
keywordsExtracted = set(rake.extract(content, incl_scores=False)[0:N])
keywordsExpected = set(listfromfilelines(keyfile)[0:N])
tp, fp, fn = confusionMatrix(keywordsExtracted, keywordsExpected);
p, r, f1 = getF1(tp, fp, fn)
tp_total += tp
fp_total += fp
fn_total += fn
print "F1 for top " + str(N) + " keywords in " + txtfile + ":\t" + str(f1)
s=""
for x in keywordsExtracted:
s = s + x +" "
wordcloud = WordCloud().generate(s)
plt.imshow(wordcloud)
plt.axis('off')
plt.show()
raw_input(">")
s=""
for x in keywordsExpected:
s = s + x +" "
wordcloud = WordCloud().generate(s)
plt.imshow(wordcloud)
plt.axis('off')
plt.show()
raw_input(">")
s=content
wordcloud = WordCloud().generate(s)
plt.imshow(wordcloud)
plt.axis('off')
plt.show()
raw_input(">")
except Exception,err:
print Exception,err
#COMMENT NEXT LINES IN FOR DEBUGGING
"""print "Extracted Keywords:"
def completeRake(content):
rakeObject = RakeKeywordExtractor(set([]))
keywordList = rakeObject.extract(content, True)
words = nltk.word_tokenize(content)
content_lemmatized = ""
words = list(map(lambda x: lemma_obj.lemmatize(x), words))
content_lemmatized = ' '.join(words)
content = content_lemmatized
freq = {}
freq_dist = nltk.FreqDist(words)
keyword_freq = getKeywordFrequency(keywordList)
adjacency_freq = getAdjacencyFrequency(keywordList, content, words)
sortedFreqList = sorted(freq_dist.items(),
key=lambda x: x[1],
reverse=True)
additional_stopwords = []
for key in sortedFreqList:
keyword_freq.setdefault(key[0], 0)
adjacency_freq.setdefault(key[0], 0)
if (adjacency_freq[key[0]] > keyword_freq[key[0]]):
additional_stopwords.append(key[0])
additional_stopwords = set(additional_stopwords)
newRakeObject = RakeKeywordExtractor(additional_stopwords)
newKeywordList = newRakeObject.extract(content)
for keywords in newKeywordList:
print keywords
def keywords(self,text,tag_table):
tagtable = open(tag_table,'r')
tags = []
features = []
wordset = self.freqwords(text)
for i in tagtable.readlines():
tags.append(i.split(',')[0])
rake = RakeKeywordExtractor()
rakelist = rake.test(text)
for i in rakelist:
features.append(self.normalise(i[0]).replace(' ','-'))
for keyword in wordset:
for phrase in features:
if phrase.find(keyword)!= -1 :
features.append(keyword)
break
intersection = set(tags) & set(features)
# print features
return ' '.join(list(intersection))
keyword_freq={}
for i in range(0,NUM):
keywords = keywordList[i][0].split(' ')
length = len(keywords)
for word in keywords:
keyword_freq.setdefault(word,0)
keyword_freq[word] = keyword_freq[word] + 1
return keyword_freq
#f = open(sys.argv[1],'r')
f = codecs.open(sys.argv[1],'r',"iso8859-15")
content = f.read()
content = content.encode('ascii','ignore')
content = re.sub(r"[1-9][0-9]*\.?[0-9]*",'',content)
rakeObject = RakeKeywordExtractor(set([]))
keywordList = rakeObject.extract(content,True)
words = nltk.word_tokenize(content)
content_lemmatized = ""
words = list(map(lambda x: lemma_obj.lemmatize(x),words))
content_lemmatized = ' '.join(words)
content = content_lemmatized
freq = {}
freq_dist = nltk.FreqDist(words)
keyword_freq=getKeywordFrequency(keywordList)
adjacency_freq = getAdjacencyFrequency(keywordList,content,words)
sortedFreqList = sorted(freq_dist.items(), key = lambda x: x[1],reverse=True)
additional_stopwords=[]
for key in sortedFreqList:
).join(
Portal,
Peticion.folioPeticion == Portal.folioSAC).join(
Temas, Peticion.tema_id == Temas.temaId
).join(
Dependencia,
Peticion.dependencia_id == Dependencia.dependenciaId
)
kw = {}
temas = {}
for pet in peticion_list:
tema_pet = pet.Temas.nomTema
if tema_pet in temas:
temas[tema_pet] = temas[tema_pet] + 1
else:
temas[tema_pet] = 1
rake = RakeKeywordExtractor()
kw_tmp = rake.extract(pet.Portal.descripcion)
for word in list(kw_tmp):
if word not in kw:
kw[word] = 1
else:
kw[word] += 1
mylist = sorted(temas.items(), key=lambda x:x[1], reverse=True)
for item in mylist:
print('"'+item[0]+'": {"peticiones_atendidas": '+str(item[1])+', "palabras_clave": "'+tema_keyword[item[0]]+'"},')
exit(-1)
working_dir = sys.argv[1] # param
question_id = int(sys.argv[2]) # param
max_tags = int(sys.argv[3]) # param
must_delete_former_automatic_tags = int(sys.argv[4]) # param
download_dir="/tmp/nltk_data"
# resources download
nltk.download('stopwords', download_dir)
nltk.download('punkt', download_dir)
nltk.download('averaged_perceptron_tagger', download_dir)
nltk.download('wordnet', download_dir)
nltk.download('pickle', download_dir)
rake = RakeKeywordExtractor();
TAG_TYPE = "AUTOMATIC"
queryTag = "SELECT ID FROM SurveyAnswerTag WHERE Type='AUTOMATIC' AND Value = '%s' "
queryInsertTag = "INSERT INTO SurveyAnswerTag (Created,LastEdited, Value, Type, CreatedByID) VALUES(NOW(), NOW(), '%s', 'AUTOMATIC', 0 )"
queryAnswers = "SELECT ID, Value FROM SurveyAnswer WHERE Value IS NOT NULL AND QuestionID = %d"
queryInsertAnswerTag = "INSERT INTO SurveyAnswer_Tags(SurveyAnswerID, SurveyAnswerTagID) VALUES(%d, %d)"
querySelectAnswerTag = "SELECT ID FROM SurveyAnswer_Tags WHERE SurveyAnswerID = %d AND SurveyAnswerTagID = %d"
queryDeleteFormerTags = "DELETE FROM SurveyAnswer_Tags WHERE SurveyAnswerID = %d AND SurveyAnswerTagID IN (SELECT ID FROM SurveyAnswerTag where Type = 'AUTOMATIC')"
config = DBConfig(working_dir+"/db.ini").read_db_config()
cursor = None
try:
# Open database connection