def learning():
lexicon_feat, embed_feat = initFeatureProcessors()
data = json.loads(request.data)
# try 'lucky @USERID ! good luck @USERID & see you soon :) @USERID @USERID'
result = parallelClassifier([data], lexicon_feat, embed_feat)
emotions = result[0]['emotions']
return jsonify(emotions)
def learning():
lexicon_feat, embed_feat = initFeatureProcessors()
data = json.loads(request.data)
# try 'lucky @USERID ! good luck @USERID & see you soon :) @USERID @USERID'
result = parallelClassifier([data], lexicon_feat, embed_feat)
emotions = result[0]['emotions']
return jsonify(emotions)
import json, os
import codecs, sys
from argparse import ArgumentParser
from Classifier import parallelClassifier, initFeatureProcessors
global lexicon_feat, embed_feat
lexicon_feat = None
embed_feat = None
initFeatureProcessors()
def read_tweets(inputdir):
files = [f for f in os.listdir(inputdir) if f.endswith(".json")]
tweets = []
for filename in files:
input_filename = os.path.join(inputdir, filename)
with codecs.open(input_filename, encoding="utf8") as tweet_file:
for line in tweet_file:
data = json.loads(line)
tweets.append(data)
return tweets
def write_results(outputdir, results):
fp = open(outputdir + "/results.json", "wb")
for result in results:
json.dump(result, fp)
fp.write("\n")
fp.close()
import json, os
import codecs, sys
from argparse import ArgumentParser
from Classifier import parallelClassifier, initFeatureProcessors
global lexicon_feat, embed_feat
lexicon_feat = None
embed_feat = None
initFeatureProcessors()
def read_tweets(inputdir):
files = [f for f in os.listdir(inputdir) if f.endswith('.json')]
tweets = []
for filename in files:
input_filename = os.path.join(inputdir, filename)
with codecs.open(input_filename, encoding='utf8') as tweet_file:
for line in tweet_file:
data = json.loads(line)
tweets.append(data)
return tweets
def write_results(outputdir, results):
fp = open(outputdir + '/results.json', 'wb')
for result in results:
json.dump(result, fp)
fp.write('\n')
fp.close()
