def read_twits(inp, output):
# read tweet ids from <inp>, write tweets (json format)
# to folder <output>.
text = read_data(inp)
was = False
for line in text:
idshka, lang = line.split(';')
while True:
try:
limits = tw.GetRateLimitStatus('statuses')[u'resources'][u'statuses'][u'/statuses/show/:id']
if limits[u'remaining'] <= 10:
time.sleep(10)
else:
break
except:
time.sleep(10)
try:
f = open(output + lang + idshka, 'r')
f.close()
except IOError:
try:
s = get_one(int(idshka))
write_data(output + lang + idshka, s.AsJsonString())
except:
print 'Twit ' + idshka + ' is hidden now'
def russian_get_text(inp, output):
# parse tweets from .csv file by Julia Rubtsova
# from 'Метод построения и анализа корпуса коротких текстов для задачи классификации отзывов'
data = read_data(inp)
res = []
pattern = '''"(.*?)";'''
for line in data:
tokens = nltk.regexp_tokenize(line, pattern)
if len(tokens) < 4:
continue
text = tokens[3][1:-2]
mentions = get_mentions(text)
links = get_links(text)
hashtags = get_hashtags(text)
text = process(text)
sname = tokens[2][1:-2]
if text == '':
continue
row = [text, 'not-given', sname, 'not-given', ','.join(hashtags), ','.join(mentions), ','.join(links)]
row = '\t'.join(row)
res.append(row)
write_data(output, res)
def bergsma_get_text(inp, output):
# extract text with features from tweet in format provided by
# Shane Bergsma from paper
# 'Language Identification for Creating Language-Specific Twitter Collections'.
data = read_data(inp)
res = []
for line in data:
tokens = line.split('\t')
if len(tokens) < 3:
continue
text = tokens[5]
mentions = get_mentions(text)
links = get_links(text)
hashtags = get_hashtags(text)
text = process(text)
sname = tokens[1]
uname = tokens[2]
location = tokens[3]
row = [text, uname, sname, location, ','.join(hashtags), ','.join(mentions), ','.join(links)]
row = '\t'.join(row)
if text == '':
continue
res.append(row)
write_data(output, res)
def bergsma_get_text(inp, output):
# extract plain text from tweet in format provided by
# Shane Bergsma from paper
# 'Language Identification for Creating Language-Specific Twitter Collections'.
data = read_data(inp)
res = []
for line in data:
tokens = line.split('\t')
if len(tokens) < 3:
continue
res.append(tokens[5])
sep = '\n' + '-' * 60 + '\n' * 4
write_data(output, res, sep)
def russian_get_text(inp, output):
# parse tweets from .csv file by Julia Rubtsova
# from 'Метод построения и анализа корпуса коротких текстов для задачи классификации отзывов'
data = read_data(inp)
res = []
pattern = '''"(.*?)";'''
for line in data:
tokens = nltk.regexp_tokenize(line, pattern)
if len(tokens) < 4:
continue
res.append(tokens[3][1:-2])
sep = '\n' + '-' * 60 + '\n' * 4
write_data(output, res, sep)
from string_processing import *
import os, os.path
import sys
import codecs
from scripts import read_data, write_data
#######################################################
# get all .txt files from $1, parse tweets from them
# (should be separated by 'separator') and write to $2
# with same file name
folder = sys.argv[1]
separator = "\n" + "-" * 60 + "\n" * 4
if not os.path.isdir(sys.argv[2]):
os.mkdir(sys.argv[2])
for fi in os.listdir(folder):
if fi.endswith(".txt"):
texts = read_data(os.path.join(folder, fi), separator)
parsed = []
for text in texts:
text = process(text)
if text != "":
parsed.append(text)
write_data(os.path.join(sys.argv[2], fi), parsed)
