def parserNegativeVerb():
with open("data/negative_verbs_dump.txt", "r") as f:
words_to_check = f.readlines()
words_to_check = [x.strip() for x in words_to_check]
sentences_containing_words = []
for sentence in sentences:
for word in words_to_check:
if sentence.lower().find(word) != -1:
sentences_containing_words.append(sentence)
break
with open('data/negative_verbs_sentences_dump.txt', 'w+') as f:
f.writelines("%s\n" % line for line in sentences_containing_words)
with open('data/negative_verbs_sentences_dump.txt', 'r') as f:
sentences = f.readlines()
sentences = [x.strip() for x in sentences]
from finnish_toolkit import parser
mp = parser.Parser()
parsed_sentences = []
for i in range(30):
sentence = sentences[i]
parsed_sentence = mp.parse(sentence)
parsed_sentences.append(parsed_sentence)
import os
dump_folder = './data/visualizations/negative_verbs'
if not os.path.exists(dump_folder):
os.makedirs(dump_folder)
for i, parsed_sentence in enumerate(parsed_sentences):
parser.visualize(
parsed_sentence, '{}/{}.html'.format(dump_folder, i))
def parse_most_frequent_named_entity_sentences():
with open('data/five_diseases_sentences_dump.txt', 'r') as f:
sentences = f.readlines()
sentences = [x.strip() for x in sentences]
from finnish_toolkit import parser
mp = parser.Parser()
parsed_sentences = []
for i in range(30):
sentence = sentences[i]
parsed_sentence = mp.parse(sentence)
parsed_sentences.append(parsed_sentence)
import os
dump_folder = './data/visualizations/five_diseases'
if not os.path.exists(dump_folder):
os.makedirs(dump_folder)
for i, parsed_sentence in enumerate(parsed_sentences):
parser.visualize(
parsed_sentence, '{}/{}.html'.format(dump_folder, i))
def getTopicVariation():
topics=[]
for fileN in listdir("./post_process/time_lemmatized"):
if fileN.endswith(".txt"):
filename="./post_process/time_lemmatized/{}".format(fileN)
else:
continue
with open(filename) as f:
print(filename)
sentencespre = f.read()
f.close()
topic=overallTopic(sentencespre)
print(topic)
topics.append(topic)
return topics
sentiment=[]
for fileN in listdir("./post_process/time"):
if fileN.endswith(".txt"):
filename="./post_process/time/{}".format(fileN)
else:
continue
with open(filename) as f:
print(filename)
sentencespre = f.read()
f.close()
sentiment_item=overallSentiment(sentencespre)
print(sentiment_item)
sentiment.append(sentiment_item)
return sentiment
with open('data/five_diseases_sentences_dump.txt', 'r') as f:
sentences = f.readlines()
sentences = [x.strip() for x in sentences]
from finnish_toolkit import parser
mp = parser.Parser()
parsed_sentences = []
for i in range(30):
sentence = sentences[i]
parsed_sentence = mp.parse(sentence)
parsed_sentences.append(parsed_sentence)
import os
dump_folder = './data/visualizations/five_diseases'
if not os.path.exists(dump_folder):
os.makedirs(dump_folder)
for i, parsed_sentence in enumerate(parsed_sentences):
parser.visualize(
parsed_sentence, '{}/{}.html'.format(dump_folder, i))
# sent = overallSentiment(sentencespre)
# overall_sent = [overallSentiment(sentence) for sentence in sentences]
# sentences=fetchSentences()
# histogramNER()
# commoncooccurred = mostCooccuring()
# print(overallTopic(". ".join(sent) for sent in sentences))
# analyzeFiles()
# translationFunc()
# getTopicVariation()
# getSentVariation()
# translateDivided()
# extract_sentences_of_most_frequent_named_entities()
# parse_most_frequent_named_entity_sentences()