def subeventpie():
"""子观点占比
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
# topic_name = u'APEC2014-微博'
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getAllNewsComments()
cluster_ratio = dict()
for comment in comments:
if 'clusterid' in comment:
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
results = dict()
total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
feature = eventcomment.get_feature_words(clusterid)
if feature and len(feature):
results[','.join(feature[:3])] = float(ratio) / float(total_count)
return json.dumps(results)
def ratio():
"""子观点占比
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
cluster_ratio = dict()
for comment in comments:
if 'clusterid' in comment:
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
results = dict()
total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
feature = eventcomment.get_feature_words(clusterid)
if feature and len(feature):
results[','.join(feature[:3])] = float(ratio) / float(total_count)
return json.dumps(results)
def sentimentpie():
"""
情绪占比
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
# topic_name = u'APEC2014-微博'
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getAllNewsComments()
senti_dict = {0: '中性', 1: '积极', 2: '愤怒', 3: '悲伤'}
senti_ratio = dict()
for comment in comments:
if 'sentiment' in comment:
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
results = dict()
total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
label = senti_dict[sentiment]
if label and len(label):
results[label] = float(ratio) / float(total_count)
return json.dumps(results)
def sentiratio():
"""
情绪占比
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
senti_dict = {
0:'中性',
1:'积极',
2:'愤怒',
3:'悲伤'
}
senti_ratio = dict()
for comment in comments:
if 'sentiment' in comment:
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
results = dict()
total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
label = senti_dict[sentiment]
if label and len(label):
results[label] = float(ratio) / float(total_count)
return json.dumps(results)
def commments():
"""
查看有无评论
"""
topic_name = request.args.get('query', default_topic_name)
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
if comments:
return json.dumps({"status": "success"})
else:
return json.dumps({"status": "fail"})
def commments():
"""
查看有无评论
"""
topic_name = request.args.get('query', default_topic_name)
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
if comments:
return json.dumps({"status":"success"})
else:
return json.dumps({"status":"fail"})
def keywords():
"""关键词
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
clusterids = eventcomment.get_cluster_ids(news_id)
results = dict()
for clusterid in clusterids:
fwords = eventcomment.get_feature_words(clusterid)
results[clusterid] = [fwords[:5], fwords]
return json.dumps(results)
def keywords():
"""关键词
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
clusterids = eventcomment.get_cluster_ids(news_id)
results = dict()
for clusterid in clusterids:
fwords = eventcomment.get_feature_words(clusterid)
results[clusterid] = [fwords[:5], fwords]
return json.dumps(results)
def index():
"""返回页面
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
news = News(news_id, topicid)
news_subeventid = news.get_news_subeventid()
news_url = news.get_news_url()
comments = eventcomment.getNewsComments(news_id)
if not comments:
return 'no comments'
return render_template('index/comment.html', topic=topic_name, topic_id=topicid, \
news_id=news_id, news_subeventid=news_subeventid, news_url=news_url)
def index():
"""返回页面
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
news = News(news_id, topicid)
news_subeventid = news.get_news_subeventid()
news_url = news.get_news_url()
comments = eventcomment.getNewsComments(news_id)
if not comments:
return 'no comments'
return render_template('index/comment.html', topic=topic_name, topic_id=topicid, \
news_id=news_id, news_subeventid=news_subeventid, news_url=news_url)
def cluster():
"""展现聚类结果
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
sort_by = request.args.get('sort', 'weight')
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
cluster_results = dict()
for comment in comments:
if 'clusterid' in comment:
clusterid = comment['clusterid']
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
'''
sentiment_dict = dict()
for clusterid, comments in cluster_results.iteritems():
positive = 0
negative = 0
for c in comments:
if c['sentiment'] == 1:
positive += 1
if c['sentiment'] in [2, 3]:
negative += 1
sentiment_dict[clusterid] = u'(积极：' + str(positive) + ',' + u'消极：' + str(negative) + ')'
'''
results = dict()
for clusterid in cluster_results:
feature = eventcomment.get_feature_words(clusterid)
if feature and len(feature):
cluster_results[clusterid].sort(key=lambda c: c[sort_by],
reverse=True)
results[clusterid] = [
','.join(feature[:5]), cluster_results[clusterid]
]
return json.dumps(results)
def urlsearch():
"""返回页面
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
topicid = em.getEventIDByName(topic_name)
news_url = request.args.get('url', default_news_url) # news url
news_url = 'http://news.sina.com.cn/c/2014-10-09/145630963839.shtml'
event = Event(topicid)
news_id = event.get_news_id_by_url(news_url)
if not news_id:
return json.dumps({"news_id":None})
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
if not comments:
return json.dumps({"news_id":None})
return json.dumps({"news_id":news_id})
def urlsearch():
"""返回页面
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
topicid = em.getEventIDByName(topic_name)
news_url = request.args.get('url', default_news_url) # news url
news_url = 'http://news.sina.com.cn/c/2014-10-09/145630963839.shtml'
event = Event(topicid)
news_id = event.get_news_id_by_url(news_url)
if not news_id:
return json.dumps({"news_id": None})
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
if not comments:
return json.dumps({"news_id": None})
return json.dumps({"news_id": news_id})
def sentiment():
"""
主观微博
"""
topic_name = request.args.get('query', default_topic_name)
# topic_name = u'APEC2014-微博'
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getAllNewsComments()
sentiment_comments = dict()
for comment in comments:
if 'sentiment' in comment:
sentiment = comment['sentiment']
try:
sentiment_comments[sentiment].append(comment)
except KeyError:
sentiment_comments[sentiment] = [comment]
return json.dumps(sentiment_comments)
def sentiment():
"""
主观微博
"""
topic_name = request.args.get('query', default_topic_name)
# topic_name = u'APEC2014-微博'
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getAllNewsComments()
sentiment_comments = dict()
for comment in comments:
if 'sentiment' in comment:
sentiment = comment['sentiment']
try:
sentiment_comments[sentiment].append(comment)
except KeyError:
sentiment_comments[sentiment] = [comment]
return json.dumps(sentiment_comments)
def cluster():
"""展现聚类结果
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
sort_by = request.args.get('sort', 'weight')
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
cluster_results = dict()
for comment in comments:
if 'clusterid' in comment:
clusterid = comment['clusterid']
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
'''
sentiment_dict = dict()
for clusterid, comments in cluster_results.iteritems():
positive = 0
negative = 0
for c in comments:
if c['sentiment'] == 1:
positive += 1
if c['sentiment'] in [2, 3]:
negative += 1
sentiment_dict[clusterid] = u'(积极：' + str(positive) + ',' + u'消极：' + str(negative) + ')'
'''
results = dict()
for clusterid in cluster_results:
feature = eventcomment.get_feature_words(clusterid)
if feature and len(feature):
cluster_results[clusterid].sort(key=lambda c: c[sort_by], reverse=True)
results[clusterid] = [','.join(feature[:5]), cluster_results[clusterid]]
return json.dumps(results)
def index():
"""返回页面
"""
topic_name = request.args.get('query', default_weibo_topic_name) # 话题名
news_id = request.args.get('news_id', default_weibo_news_id)
topicid = em.getEventIDByName(topic_name)
cluster_num = request.args.get('cluster_num', default_cluster_num)
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
calculation_label = int(request.args.get('calcu', 1)) # 默认进行重新计算, 0表示从从已有结果数据文件加载数据
news = News(news_id, topicid)
news_subeventid = news.get_news_subeventid()
if not news_subeventid:
news_subeventid = 'None'
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
return render_template('index/weibo.html', topic=topic_name, topic_id=topicid, \
news_id=news_id, news_subeventid=news_subeventid, cluster_num=cluster_num,\
cluster_eva_min_size=cluster_eva_min_size, vsm=vsm, calc_label=calculation_label)
def index():
"""返回页面
"""
topic_name = request.args.get('query', default_weibo_topic_name) # 话题名
# topic_name = u'APEC2014-微博'
news_id = request.args.get('news_id', default_weibo_news_id)
topicid = em.getEventIDByName(topic_name)
min_cluster_num = request.args.get('min_cluster_num', default_min_cluster_num)
max_cluster_num = request.args.get('max_cluster_num', default_max_cluster_num)
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
news = News(news_id, topicid)
news_subeventid = news.get_news_subeventid()
if not news_subeventid:
news_subeventid = 'None'
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
return render_template('index/weibo.html', topic=topic_name, topic_id=topicid, \
news_id=news_id, news_subeventid=news_subeventid, min_cluster_num=min_cluster_num,\
max_cluster_num=max_cluster_num, cluster_eva_min_size=cluster_eva_min_size, vsm=vsm)
def sentiment():
"""评论情绪
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
sort_by = request.args.get('sort', 'weight')
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
sentiment_comments = dict()
for comment in comments:
if 'sentiment' in comment:
sentiment = comment['sentiment']
try:
sentiment_comments[sentiment].append(comment)
except KeyError:
sentiment_comments[sentiment] = [comment]
for sentiment in sentiment_comments:
sentiment_comments[sentiment].sort(key=lambda c:c[sort_by], reverse=True)
return json.dumps(sentiment_comments)
def sentiment():
"""评论情绪
"""
topic_name = request.args.get('query', default_topic_name) # 话题名
news_id = request.args.get('news_id', default_news_id)
sort_by = request.args.get('sort', 'weight')
topicid = em.getEventIDByName(topic_name)
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
sentiment_comments = dict()
for comment in comments:
if 'sentiment' in comment:
sentiment = comment['sentiment']
try:
sentiment_comments[sentiment].append(comment)
except KeyError:
sentiment_comments[sentiment] = [comment]
for sentiment in sentiment_comments:
sentiment_comments[sentiment].sort(key=lambda c: c[sort_by],
reverse=True)
return json.dumps(sentiment_comments)
def comments_list():
if os.path.exists(temp_file):
os.remove(temp_file)
AB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../public/')
sys.path.append(AB_PATH)
from comment_module import comments_calculation_v2
topicid = request.args.get('topicid', default_topic_id)
subeventid = request.args.get('subeventid', 'global')
min_cluster_num = request.args.get('min_cluster_num', default_min_cluster_num)
max_cluster_num = request.args.get('max_cluster_num', default_max_cluster_num)
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
ec = EventComments(topicid)
if subeventid == 'global':
comments = ec.getAllNewsComments()
else:
comments = ec.getCommentsBySubeventid(subeventid)
if not comments:
return json.dumps({"status":"fail"})
cal_results = comments_calculation_v2(comments, int(min_cluster_num), int(max_cluster_num), int(cluster_eva_min_size), vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
senti_dict = {
0:'中性',
1:'积极',
2:'愤怒',
3:'悲伤'
}
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in senti_dict) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in senti_dict:
label = senti_dict[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}));
dump_file.close();
return json.dumps({"ratio":ratio_results, "sentiratio":sentiratio_results,})
def comments_list():
taskid = request.args.get('taskid', default_task_id)
cluster_num = request.args.get('cluster_num', '') #若无此参数，取-1；否则取用户设定值
if cluster_num == '':
cluster_num = default_cluster_num
cluster_eva_min_size = request.args.get('cluster_eva_min_size',
default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
temp_file = taskid + temp_file_post
if os.path.exists(temp_file):
os.remove(temp_file)
ec = EventComments(taskid)
comments = ec.getAllNewsComments()
if not comments:
return json.dumps({"status": "fail"})
cal_results = comments_calculation_v2(
comments, cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
senti_dict = {0: '中性', 1: '积极', 2: '愤怒', 3: '悲伤'}
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid'
in comment) and (comment['clusterid'][:8] != 'nonsense'):
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in senti_dict) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(
feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in senti_dict:
label = senti_dict[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(
sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict, "ratio":ratio_results, "sentiratio": sentiratio_results}))
dump_file.close()
return json.dumps({
"ratio": ratio_results,
"sentiratio": sentiratio_results
})
def comments_list():
if os.path.exists(temp_file):
os.remove(temp_file)
AB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../../public/')
sys.path.append(AB_PATH)
from comment_module import comments_calculation_v2
topicid = request.args.get('topicid', default_topic_id)
subeventid = request.args.get('subeventid', 'global')
min_cluster_num = request.args.get('min_cluster_num',
default_min_cluster_num)
max_cluster_num = request.args.get('max_cluster_num',
default_max_cluster_num)
cluster_eva_min_size = request.args.get('cluster_eva_min_size',
default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
ec = EventComments(topicid)
if subeventid == 'global':
comments = ec.getAllNewsComments()
else:
comments = ec.getCommentsBySubeventid(subeventid)
if not comments:
return json.dumps({"status": "fail"})
cal_results = comments_calculation_v2(comments, int(min_cluster_num),
int(max_cluster_num),
int(cluster_eva_min_size), vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
senti_dict = {0: '中性', 1: '积极', 2: '愤怒', 3: '悲伤'}
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid'
in comment) and (comment['clusterid'][:8] != 'nonsense'):
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in senti_dict) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(
feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in senti_dict:
label = senti_dict[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(
sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}))
dump_file.close()
return json.dumps({
"ratio": ratio_results,
"sentiratio": sentiratio_results,
})
def ratio():
"""子观点占比
"""
if os.path.exists(temp_file):
os.remove(temp_file)
topic_name = request.args.get('query', default_weibo_topic_name) # 话题名
news_id = request.args.get('news_id', default_weibo_news_id)
topicid = em.getEventIDByName(topic_name)
cluster_num = request.args.get('cluster_num', default_cluster_num)
if cluster_num == default_cluster_num:
cluster_num = -1
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
calculation_label = int(request.args.get('calcu', 1)) # 默认进行重新计算, 0表示从从已有结果数据文件加载数据
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
if not comments:
return json.dumps({"status":"fail"})
cal_results = comments_calculation_v2(comments, cluster_num=cluster_num, \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) \
and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}));
dump_file.close();
return json.dumps({"ratio":ratio_results, "sentiratio":sentiratio_results,})
def comments_list():
"""计算饼图数据，并将饼图数据和去重后的推荐文本写到文件
"""
topicid = request.args.get('topicid', default_topic_id)
subeventid = request.args.get('subeventid', 'global')
cluster_num = request.args.get('cluster_num', default_cluster_num)
if cluster_num == default_cluster_num:
cluster_num = -1
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
ec = EventComments(topicid)
if subeventid == 'global':
comments = ec.getAllNewsComments()
else:
comments = ec.getCommentsBySubeventid(subeventid)
if not comments:
return json.dumps({"status":"fail"})
cal_results = comments_calculation_v2(comments, cluster_num=int(cluster_num), \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}));
dump_file.close();
return json.dumps({"ratio":ratio_results, "sentiratio":sentiratio_results,})