def predictEmotion(tweet, fs, model):
vectors = []
vector = Mix_IG.vectorize(fs, tweet)
vectors.append(vector)
labels = [0]
m_happy, p_acc, p_happy = svm_predict(labels, vectors, happy_model, '-b 1')
m_sad, p_acc, p_sad = svm_predict(labels, vectors, sad_model, '-b 1')
m_angry, p_acc, p_angry = svm_predict(labels, vectors, angry_model, '-b 1')
m_ashamed, p_acc, p_ashamed = svm_predict(labels, vectors, ashamed_model, '-b 1')
m_afraid, p_acc, p_afraid = svm_predict(labels, vectors, afraid_model, '-b 1')
tempZ = []
tempZ.append(p_angry[0][1])
tempZ.append(p_happy[0][1])
tempZ.append(p_sad[0][1])
tempZ.append(p_ashamed[0][1])
tempZ.append(p_afraid[0][0])
print max(tempZ)
if(max(tempZ)<0.6):
return "neutral"
m, p_acc, p_vals = predict(labels, vectors, model)
map = {1 : "happy", 0 : "angry", 2 : "sad", 3 : "ashamed", 4 : "afraid"}
return map[m[0]]
import crawler
sys.path.append('/Users/oliverfengpet/Program/nlp/python_parser/no_index/libsvm-3.12/python/')
sys.path.append('/Users/oliverfengpet/Dropbox/TwitterAffect/liblinear-1.93/python')
from svmutil import *
from liblinearutil import *
# define (url -> python) mapping in urls.py
# (r'^$', index.print_welcome)
# http://127.0.0.1:8000/
happy_model = svm_load_model('libsvm_SVC_stem_emoticons_Happy.model')
sad_model = svm_load_model('libsvm_SVC_stem_emoticons_Sad.model')
ashamed_model = svm_load_model('libsvm_SVC_stem_emoticons_Ashamed.model')
angry_model = svm_load_model('libsvm_SVC_stem_emoticons_Angry.model')
afraid_model = svm_load_model('libsvm_SVC_stem_emoticons_Afraid.model')
model = load_model('libsvm_SVC_Mix.model')
bi = Mix_IG.load_bi("IG_Bigram.txt")
fs = Mix_IG.create_feature_space_IG(bi)
def print_welcome(request):
html = "<html>" + get_style() + \
"<body><div id=\"wrapper\">" \
"CIS630 Project 2 - Twitter Affect" + \
"<br>Tao Feng Shang CC le ya!" + \
"<br><a href=\"crawler\">See Random Tweets Classification</a>" \
"</div></body></html>"
return HttpResponse(html)
# (r'^crawler/$', index.print_crawling)
# http://127.0.0.1:8000/crawler/