def show_person_meaning_contrast(self, school): print school,"person" weiboid = get_random_weiboid(school) weibo_content_results = tp.get_one_weibo_data(schoolname=school, weiboid=weiboid) mydocdialog = DocDialog(contents=weibo_content_results) mydocdialog.open() weibo_interm.show_person_meaning_contrast(school, weiboid)
def show_person_meaning_contrast(self, school):
print school, "person"
weiboid = get_random_weiboid(school)
weibo_content_results = tp.get_one_weibo_data(schoolname=school,
weiboid=weiboid)
mydocdialog = DocDialog(contents=weibo_content_results)
mydocdialog.open()
weibo_interm.show_person_meaning_contrast(school, weiboid)
def all_weibo_sentence_sentiment_score(schoolname, weiboid=None):
weibo_content_results = tp.get_one_weibo_data(schoolname, weiboid)
cuted_weibo = []
upvotes = []
forwards = []
reviews = []
weiboid = None
for cell in weibo_content_results:
cuted_weibo.append(tp.cut_sentences_2(cell[0]))
upvotes.append(int(cell[1]))
forwards.append(int(cell[2]))
reviews.append(int(cell[3]))
weiboid = cell[4]
'''
print cell[0] #微博内容
'''
#print cell[1], #点赞数量
#print cell[2] #转发数量
#print cell[3] #评论数量
single_weibo_count = []
all_weibo_count = []
for index,weibo in enumerate(cuted_weibo):
for sent in weibo:
seg_sent = tp.segmentation(sent, 'list')
i = 0 # word position counter
a = 0 # sentiment word position
poscount = 0 # count a positive word
negcount = 0 # count a negative word
for word in seg_sent:
if word in posdict:
poscount += 1
for w in seg_sent[a:i]:
poscount = match(w, poscount)
a = i + 1
elif word in negdict:
negcount += 1
for w in seg_sent[a:i]:
negcount = match(w, negcount)
a = i + 1
elif word == '!'.decode('utf-8') or word == '！'.decode('utf-8'):
for w2 in seg_sent[::-1]:
if w2 in posdict:
poscount += 2
break
elif w2 in negdict:
negcount += 2
break
i += 1
single_weibo_count.append(transform_to_positive_num(poscount, negcount, upvotes[index], forwards[index], reviews[index])) #[[s1_score], [s2_score], ...]
all_weibo_count.append(single_weibo_count) # [[[s11_score], [s12_score], ...], [[s21_score], [s22_score], ...], ...]
single_weibo_count = []
return all_weibo_count, weiboid
def weiboMeaningPieChart(schoolname, weiboid=None):
weibo_content_results = tp.get_one_weibo_data(schoolname=schoolname, weiboid=weiboid)
if weibo_content_results is None:
print "There is no data about %s yet." % schoolname
else:
meaningful = [0,0]
weiboid = None
for w in weibo_content_results:
weiboid = w[4]
if tp.is_meaningful(w[0]) == True:
meaningful[0] += 1
else:
meaningful[1] += 1
meaningful[0] = math.floor((meaningful[0]*1.0/(meaningful[0]+meaningful[1]))*100)
meaningful[1] = 100.0 - meaningful[0]
labels = 'Meaningful','Meaningless' # 对应每一块的标志
explode = (0, 0.05) # 离开整体的距离，看效果
colors = ('#9999ff','#ff9999')
pietitle = "%s's %s's meaningful analysis" % (schoolname, weiboid)
weiboPieChart(pielist=meaningful, labels=labels, explode=explode, colors=colors, pietitle=pietitle)
