def preprocess(self, corpus, language='ko'):
pipeline = None
if language == 'ko':
mecab_path = 'C:\\mecab\\mecab-ko-dic'
pipeline = pre.Pipeline(
pre.splitter.NLTK(), pre.tokenizer.MeCab(mecab_path),
pre.helper.POSFilter('NN*'), pre.helper.SelectWordOnly(),
pre.ngram.NGramTokenizer(1, 2),
pre.helper.StopwordFilter(file='../../stopwordsKor.txt'))
elif language == 'en':
pipeline = pre.Pipeline(
pre.splitter.NLTK(), pre.tokenizer.WordPos(),
pre.helper.POSFilter('NN*'), pre.helper.SelectWordOnly(),
pre.ngram.NGramTokenizer(1, 2),
pre.helper.StopwordFilter(file='../../stopwordsEng.txt'))
result = pipeline.processCorpus(corpus)
print('== ==')
documents = []
for doc in result:
document = ''
for sent in doc:
document += " ".join(sent)
documents.append(document)
return documents
def process_by_date_range(startDate, endDate):
filter_by_date_range(startDate, endDate)
user_dict = './user_dic.txt'
# TODO: Customize pre-processing pipeline
pipeline = pre.Pipeline(pre.splitter.NLTK(),
pre.tokenizer.WordPos(),
pre.lemmatizer.WordNet(),
pre.helper.POSFilter('N*|J*|R*|V*'),
pre.helper.SelectWordOnly(),
pre.helper.StopwordFilter(file='./stopwordsEng.txt'),
pre.ngram.NGramTokenizer(1, 2),
pre.counter.WordCounter())
filePath1 = "./data/date_from" + startDate.strftime("%Y%m%d") + "to" + endDate.strftime("%Y%m%d") + "_data.txt"
corpus = pre.CorpusFromFieldDelimitedFile(filePath1, 0)
if os.path.exists(filePath1):
# os.remove(filePath1)
print(filePath1, "is now being processed!\n")
else:
print("File does not exist!")
result = pipeline.processCorpus(corpus)
print(result)
print()
doc_collection = ''
term_counts = {}
for doc in result:
for sent in doc:
for _str in sent:
term_counts[_str[0]] = term_counts.get(_str[0], 0) + int(_str[1])
freq = range(int(_str[1]))
co = ''
for n in freq:
co += ' ' + _str[0]
doc_collection += ' ' + co
word_freq = []
for key, value in term_counts.items():
word_freq.append((value, key))
word_freq.sort(reverse=True)
print(word_freq)
filePath2 = "./result/date_from" + startDate.strftime("%Y%m%d") + "to" + endDate.strftime("%Y%m%d") + "_result.txt"
f = open(filePath2, "w", encoding='utf8')
for pair in word_freq:
f.write(pair[1] + '\t' + str(pair[0]) + '\n')
f.close()
return doc_collection
def __init__(self, records_file, batch_size, image_shape):
"""
Args:
records_file: The TFRecords file to read data from.
batch_size: The size of batches to read.
image_shape: The shape of images to load. """
if not accessible_path(records_file):
# If we don't check this, TensorFlow gives us a really confusing and
# hard-to-debug error later on.
raise ValueError("File '%s' does not exist." % (records_file))
if len(image_shape) != 3:
raise ValueError("Image shape must be of length 3.")
self._image_shape = image_shape
self._records_file = records_file
self._batch_size = batch_size
# Create a default preprocessing pipeline.
self.__pipeline = preprocess.Pipeline()
# -*- encoding:utf8 -*-
import preprocess as pre
import networkx as nx
from matplotlib import pyplot as plt
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib as mpl
if __name__ == '__main__':
corpus = pre.CorpusFromFieldDelimitedFile('../data/ALLre_date_content.txt',
1)
pipeline = pre.Pipeline(
pre.splitter.NLTK(), pre.tokenizer.WordPos(), pre.lemmatizer.WordNet(),
pre.helper.POSFilter('N*', 'V*', 'J*'), pre.helper.SelectWordOnly(),
pre.helper.StopwordFilter(file='../stopwordsEng.txt'))
# pre.ngram.NGramTokenizer(1, 2))
result = pipeline.processCorpus(corpus)
print('== 전처리 완료 ==')
print(result)
print()
file = open('ALLre_all_AVJ_pre.txt', 'w')
file.write(result)
file.close()
print('== ==')
documents = []
dump(dataset, open(filename, 'wb'))
print('Saved: %s' % filename)
def load_dataset(self, filename):
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
return loaded_model
if __name__ == '__main__':
_negative_docs = pre.CorpusFromDirectory('../txt_sentoken/neg', True)
_positive_docs = pre.CorpusFromDirectory('../txt_sentoken/pos', True)
pipeline = pre.Pipeline(pre.splitter.NLTK(),
pre.tokenizer.Word(),
pre.helper.StopwordFilter(file='../../stopwordsEng.txt'),
pre.stemmer.Porter())
_neg_result = pipeline.processCorpus(_negative_docs)
_pos_result = pipeline.processCorpus(_positive_docs)
print('== Splitting Sentence + Tokenizing + Stopwords Removal + Stemming : Porter ==')
print(_neg_result)
print()
negative_docs = list()
for doc in _neg_result:
new_doc = []
for sent in doc:
for _str in sent:
if len(_str) > 0:
new_doc.append(_str)
negative_docs.append(' '.join(new_doc))
def getSentimentScoreByFile(filePath, windowSize=1):
if windowSize < 0:
print("Wrong window size")
exit(1)
corpus = pre.CorpusFromFile(filePath)
pipeline = pre.Pipeline(
pre.splitter.NLTK(),
# pre.tokenizer.WordPos(),
pre.tokenizer.Word(),
pre.helper.StopwordFilter(file='../stopwordsEng.txt'),
pre.tagger.NLTK(),
pre.lemmatizer.WordNet())
result = pipeline.processCorpus(corpus)
print(result)
EnglishDictionarySentimentAnalyzer().createDictionary()
final_grand_score = 0 # file level
final_count = 0 # file level
final_score_array = [] # file level
for document in result:
convertedDocument = document
# merge sentences in each document by window size
if windowSize > 1:
sentences = []
for sent in document:
sentences.append(sent)
if len(sentences) < windowSize:
print("Window size is larger than the number of sentences")
print(
"Window size will be set as 1 (default) for this document")
else:
newArray = []
for a in range(0, len(sentences) - windowSize + 1):
tempArray = []
for b in range(0, windowSize):
for element in sentences[a + b]:
tempArray.append(element)
# print("tempArray: ", end="")
# print(tempArray)
newArray.append(tempArray)
# print("newArray: ", end="")
# print(newArray)
convertedDocument = newArray
grand_score, count = getSentimentScoreByDocument(convertedDocument)
if count > 0:
doc_avg_score = grand_score / count
print("Average Sentiment Score: " + str(doc_avg_score))
final_grand_score += doc_avg_score
final_count += 1
final_score_array.append(str(doc_avg_score))
else:
print("This document is empty")
try:
final_avg_score = final_grand_score / final_count
return str(final_avg_score), final_score_array
except ZeroDivisionError:
return str(0), ["This file is empty"]
def getKeywordSentimentScoreByFile(filePath, windowSize=1):
if windowSize < 0:
print("Wrong window size")
exit(1)
corpus = pre.CorpusFromFile(filePath)
pipeline = pre.Pipeline(
pre.splitter.NLTK(),
# pre.tokenizer.WordPos(),
pre.tokenizer.Word(),
pre.helper.StopwordFilter(file='../stopwordsEng.txt'),
pre.tagger.NLTK(),
pre.lemmatizer.WordNet())
result = pipeline.processCorpus(corpus)
#print(result)
EnglishDictionarySentimentAnalyzer().createDictionary()
final_grand_score = 0 # file level
final_count = 0 # file level
final_score_array = [] # file level
for document in result:
#convertedDocument = document
# merge sentences in each document by window size
# filter sentences by keyword
if windowSize > 1:
sentences = []
keyword = ["Korea", "Koreans", "Korean"]
for sent in document:
num = 0
sentences1 = []
while num < len(sent):
k = 0
while k < len(keyword):
if keyword[k] in sent[num]:
if document.index(sent) > windowSize:
sents = document[document.index(sent) -
windowSize:document.
index(sent) + windowSize]
else:
sents = document[0:document.index(sent) +
windowSize]
sentences1.extend(sentences)
sentences.extend(sents)
break
else:
k += 1
pass
if sentences1 != sentences:
break
else:
num += 1
pass
#print("===sentences===")
#print(sentences)
convertedDocument = sentences
grand_score, count = getSentimentScoreByDocument(convertedDocument)
if count > 0:
doc_avg_score = grand_score / count
print("Average Sentiment Score: " + str(doc_avg_score))
final_grand_score += doc_avg_score
final_count += 1
final_score_array.append(str(doc_avg_score))
else:
print("This document is empty")
try:
final_avg_score = final_grand_score / final_count
return str(final_avg_score), final_score_array
except ZeroDivisionError:
return str(0), ["This file is empty"]
#!/usr/bin/python3
# Author: Suzanna Sia
### Standard imports
import numpy as np
import pdb
import os
import sys
import json
import preprocess
INDEX = "coe"
pipe = preprocess.Pipeline()
def docs_to_json(target_doc_fol):
jsonl = []
for fil in os.listdir(target_doc_fol):
dd = {}
with open(os.path.join(target_doc_fol, fil), 'r') as f:
text = f.readlines()
fn = fil[:fil.find('.')]
dd['_id'] = fn
dd['doc_text'] = pipe.strip_clean(" ".join(text))
dd['docid'] = fn
dd['_index'] = "coe" # refac
jsonl.append(dd)
return jsonl
file_name = './data/emo_positive.txt'
sentiAnalyzer.readPositiveEmotiDictionary(file_name)
file_name = './data/polarity.csv'
sentiAnalyzer.readPolarityDictionary(file_name)
dict_list = sentiAnalyzer.getSentiDictionary()
pipeline = None
# corpus = pre.CorpusFromFieldDelimitedFile('../data/donald.txt', 2)
mecab_path = 'C:\\mecab\\mecab-ko-dic'
mode = 'korean_lemmatizer'
if mode is not 'korean_lemmatizer':
pipeline = pre.Pipeline(
pre.splitter.NLTK(),
pre.tokenizer.MeCab(mecab_path),
# pre.tokenizer.Komoran(),
pre.helper.SelectWordOnly(),
pre.ngram.NGramTokenizer(1, 2, concat=' '),
pre.helper.StopwordFilter(file='../stopwordsKor.txt'))
else:
pipeline = pre.Pipeline(
pre.splitter.NLTK(),
pre.tokenizer.MeCab(mecab_path),
# pre.tokenizer.Komoran(),
pre.lemmatizer.SejongPOSLemmatizer(),
pre.helper.SelectWordOnly(),
# pre.ngram.NGramTokenizer(1, 2, concat=' ')),
pre.helper.StopwordFilter(file='../stopwordsKor.txt'))
# documents = ['오늘은 비가와서 그런지 매우 우울하다',
# '시험이 끝나야 놀지 스트레스 받아ㅠㅠ',
return _train_negative_docs, _train_positive_docs, _test_negative_docs, _test_positive_docs, labels
if language == 'en':
_train_negative_docs, _train_positive_docs, _test_negative_docs, _test_positive_docs \
= read_english_corpus()
elif language == 'ko':
_train_negative_docs, _train_positive_docs, _test_negative_docs, _test_positive_docs, labels \
= read_korean_corpus()
if language == 'ko':
mecab_path = 'C:\\mecab\\mecab-ko-dic'
pipeline = pre.Pipeline(pre.splitter.NLTK(),
pre.tokenizer.MeCab(mecab_path),
pre.helper.POSFilter('NN*'),
pre.helper.SelectWordOnly(),
pre.ngram.NGramTokenizer(1, 2),
pre.helper.StopwordFilter(file='../../stopwordsKor.txt')
)
elif language == 'en':
pipeline = pre.Pipeline(pre.splitter.NLTK(),
pre.tokenizer.WordPos(),
pre.helper.POSFilter('NN*|A*|V*|J*'),
pre.helper.SelectWordOnly(),
# pre.ngram.NGramTokenizer(1, 2),
pre.helper.StopwordFilter(file='../../stopwordsEng.txt')
)
def make_documents(result):
docs = []