def text_kmeans_clustering():
# 聚类评价时选取TOPK_FREQ_WORD的高频词
TOPK_FREQ_WORD = 50
# 聚类评价时最小簇的大小
LEAST_SIZE = 8
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
results = eventcomment.getNewsComments(news_id)
inputs = []
for r in results:
r['title'] = ''
r['content'] = r['content168'].encode('utf-8')
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 情绪计算
for r in inputs:
sentiment = triple_classifier(r)
comment = Comment(r['_id'])
comment.update_comment_sentiment(sentiment)
# kmeans 聚类及评价
kmeans_results = kmeans(inputs, k=10)
reserve_num = 5
final_cluster_results, tfidf_dict = cluster_evaluation(kmeans_results, \
top_num=reserve_num, topk_freq=TOPK_FREQ_WORD, least_size=LEAST_SIZE, min_tfidf=None)
inputs = []
for label, items in final_cluster_results.iteritems():
if label != 'other':
inputs.extend(items)
for item in items:
news = News(item['news_id'])
if label == 'other':
label = news.otherClusterId
comment = Comment(item['_id'])
comment.update_comment_label(label)
eventcomment.save_cluster(label, news_id, int(time.time()))
#计算各簇特征词
cluster_feature = extract_feature(inputs)
for label, fwords in cluster_feature.iteritems():
eventcomment.update_feature_words(label, fwords)
#计算文本权重
for input in inputs:
weight = text_weight_cal(input, cluster_feature[input['label']])
comment = Comment(input['_id'])
comment.update_comment_weight(weight)
def text_kmeans_clustering():
# 聚类评价时选取TOPK_FREQ_WORD的高频词
TOPK_FREQ_WORD = 50
# 聚类评价时最小簇的大小
LEAST_SIZE = 8
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
results = eventcomment.getNewsComments(news_id)
inputs = []
for r in results:
r['title'] = ''
r['content'] = r['content168'].encode('utf-8')
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 情绪计算
for r in inputs:
sentiment = triple_classifier(r)
comment = Comment(r['_id'])
comment.update_comment_sentiment(sentiment)
# kmeans 聚类及评价
kmeans_results = kmeans(inputs, k=10)
reserve_num = 5
final_cluster_results, tfidf_dict = cluster_evaluation(kmeans_results, \
top_num=reserve_num, topk_freq=TOPK_FREQ_WORD, least_size=LEAST_SIZE, min_tfidf=None)
inputs = []
for label, items in final_cluster_results.iteritems():
if label != 'other':
inputs.extend(items)
for item in items:
news = News(item['news_id'])
if label == 'other':
label = news.otherClusterId
comment = Comment(item['_id'])
comment.update_comment_label(label)
eventcomment.save_cluster(label, news_id, int(time.time()))
#计算各簇特征词
cluster_feature = extract_feature(inputs)
for label, fwords in cluster_feature.iteritems():
eventcomment.update_feature_words(label, fwords)
#计算文本权重
for input in inputs:
weight = text_weight_cal(input, cluster_feature[input['label']])
comment = Comment(input['_id'])
comment.update_comment_weight(weight)
def load_object_weibo_data():
"""加载主观微博数据
"""
# topicname = u'外滩踩踏-微博'
# topicname = u'呼格案-微博'
# topicname = u'复旦投毒案-微博'
# topicname = u'APEC-微博'
# topicname = u'高校宣传思想工作-微博'
topicname = u'张灵甫遗骨被埋羊圈-微博'
em = EventManager()
topicid = em.getEventIDByName(topicname)
print topicid
eventcomment = EventComments(topicid)
# f = open('caitai.jl')
# f = open('huge.jl')
# f = open('fudan.jl')
# f = open('apec.jl')
# f = open('items_qiushi.jl')
f = open('items_zhang.jl')
for line in f:
item = json.loads(line.strip())
if 'mid' in item:
item['text'] = item['text'].encode('utf-8')
item = subob_classifier(item)
if item['subject']:
comment = api_object_weibo2comment(item)
eventcomment.saveItem(comment)
"""
item['text'] = item['text'].encode('utf-8')
item = subob_classifier(item)
if item['subject']:
weibo = object_weibo2comment(item)
eventcomment.saveItem(weibo)
"""
f.close()
def load_object_weibo_data():
"""加载主观微博数据
"""
# topicname = u'外滩踩踏-微博'
# topicname = u'呼格案-微博'
# topicname = u'复旦投毒案-微博'
# topicname = u'APEC-微博'
# topicname = u'高校宣传思想工作-微博'
topicname = u'张灵甫遗骨被埋羊圈-微博'
em = EventManager()
topicid = em.getEventIDByName(topicname)
print topicid
eventcomment = EventComments(topicid)
# f = open('caitai.jl')
# f = open('huge.jl')
# f = open('fudan.jl')
# f = open('apec.jl')
# f = open('items_qiushi.jl')
f = open('items_zhang.jl')
for line in f:
item = json.loads(line.strip())
if 'mid' in item:
item['text'] = item['text'].encode('utf-8')
item = subob_classifier(item)
if item['subject']:
comment = api_object_weibo2comment(item)
eventcomment.saveItem(comment)
"""
item['text'] = item['text'].encode('utf-8')
item = subob_classifier(item)
if item['subject']:
weibo = object_weibo2comment(item)
eventcomment.saveItem(weibo)
"""
f.close()
def initializeWeiboTopic():
"""初始化weibo话题
"""
em = EventManager()
# topicname = u'外滩踩踏-微博'
# start_datetime = "2015-01-02 00:00:00"
# topicname = u'呼格案-微博'
# start_datetime = "2014-12-14 00:00:00"
# topicname = u'复旦投毒案-微博'
# start_datetime = "2014-12-15 00:00:00"
# topicname = u'APEC-微博'
# start_datetime = "2014-12-15 00:00:00"
# topicname = u'高校宣传思想工作-微博'
# start_datetime = "2015-01-30 00:00:00"
topicname = u'高校宣传思想工作-repost微博'
start_datetime = "2015-01-30 00:00:00"
topicid = em.getEventIDByName(topicname)
start_ts = datetime2ts(start_datetime)
eventcomment = EventComments(topicid)
eventcomment.initialize(start_ts)
def initializeWeiboTopic():
"""初始化weibo话题
"""
em = EventManager()
# topicname = u'外滩踩踏-微博'
# start_datetime = "2015-01-02 00:00:00"
# topicname = u'呼格案-微博'
# start_datetime = "2014-12-14 00:00:00"
# topicname = u'复旦投毒案-微博'
# start_datetime = "2014-12-15 00:00:00"
# topicname = u'APEC-微博'
# start_datetime = "2014-12-15 00:00:00"
# topicname = u'高校宣传思想工作-微博'
# start_datetime = "2015-01-30 00:00:00"
topicname = u'高校宣传思想工作-repost微博'
start_datetime = "2015-01-30 00:00:00"
topicid = em.getEventIDByName(topicname)
start_ts = datetime2ts(start_datetime)
eventcomment = EventComments(topicid)
eventcomment.initialize(start_ts)
def one_topic_calculation_comments_v2(topicid):
"""对评论进行聚类
"""
from comment_clustering_tfidf_v2 import kmeans, tfidf_v2, text_classify, cluster_evaluation, global_text_weight
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
eventcomment.clear_cluster(news_id)
results = eventcomment.getNewsComments(news_id)
news = News(news_id)
inputs = []
for r in results:
r['title'] = ''
r['content'] = r['content168'].encode('utf-8')
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 情绪计算
for r in inputs:
sentiment = triple_classifier(r)
comment = Comment(r['_id'], topicid)
comment.update_comment_sentiment(sentiment)
tfidf_word = tfidf_v2(inputs)
#聚类个数=过滤后文本数/2向上取整，大于10的取10
kmeans_cluster_number = int(math.ceil(float(len(inputs)) / 5.0))
if kmeans_cluster_number > 10:
kmeans_cluster_number = 10
if kmeans_cluster_number < 5:
kmeans_cluster_number = 5
# 评论词聚类
word_label = kmeans(tfidf_word, inputs, k=kmeans_cluster_number)
# 计算全局文本权重
for r in inputs:
gweight = global_text_weight(r['content'], tfidf_word)
comment = Comment(r['_id'], topicid)
comment.update_comment_global_weight(gweight)
# 评论文本分类
results = text_classify(inputs, word_label, tfidf_word)
#簇评价
reserved_num = int(math.ceil(float(kmeans_cluster_number) / 2.0))
LEAST_CLUSTER_SIZE = 3 # 最小的簇大小
TOPK_FREQ = 10
TOPK_WEIGHT = 5
LEAST_FREQ = 0
final_cluster_results = cluster_evaluation(results, top_num=reserved_num, topk_freq=TOPK_FREQ, \
least_freq=LEAST_FREQ, least_size=LEAST_CLUSTER_SIZE, topk_weight=TOPK_WEIGHT)
for label, items in final_cluster_results.iteritems():
if label == 'other':
label = news.otherClusterId
if len(items):
eventcomment.save_cluster(label, news_id, int(time.time()))
if label != news.otherClusterId:
fwords = word_label[label]
eventcomment.update_feature_words(label, fwords)
for item in items:
comment = Comment(item['_id'], topicid)
comment.update_comment_label(label)
comment.update_comment_weight(item['weight'])
def one_topic_calculation_comments_v7(topicid):
"""对评论进行聚类
"""
from comment_clustering_tfidf_v7 import tfidf_v2, text_classify, \
cluster_evaluation, choose_cluster
from weibo_subob_rub_neu_classifier import weibo_subob_rub_neu_classifier
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
eventcomment.clear_cluster(news_id)
results = eventcomment.getNewsComments(news_id)
news = News(news_id)
# 数据字段预处理
inputs = []
for r in results:
r['title'] = ''
r['content168'] = r['content168'].encode('utf-8')
r['content'] = r['content168']
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 去除垃圾和新闻文本
items = weibo_subob_rub_neu_classifier(inputs)
inputs = []
for item in items:
subob_rub_neu_label = item['subob_rub_neu_label']
if not subob_rub_neu_label in [1, 0]:
# 1表示垃圾文本，0表示新闻文本
inputs.append(item)
MIN_CLUSTERING_INPUT = 30
MIN_CLUSTER_NUM = 2
MAX_CLUSTER_NUM = 10
# TFIDF词、聚类数量自动选择、vsm作属性也要可设成参数
if len(inputs) >= MIN_CLUSTERING_INPUT:
tfidf_word, input_dict = tfidf_v2(inputs)
results = choose_cluster(tfidf_word, inputs, MIN_CLUSTER_NUM, MAX_CLUSTER_NUM)
#评论文本聚类
cluster_text = text_classify(inputs, results, tfidf_word)
evaluation_inputs = []
for k,v in enumerate(cluster_text):
inputs[k]['label'] = v['label']
inputs[k]['weight'] = v['weight']
evaluation_inputs.append(inputs[k])
#簇评价
recommend_text = cluster_evaluation(evaluation_inputs)
for label, items in recommend_text.iteritems():
if label == 'other':
label = news.otherClusterId
if len(items):
eventcomment.save_cluster(label, news_id, int(time.time()))
if label != news.otherClusterId:
fwords = results[label]
eventcomment.update_feature_words(label, fwords)
for item in items:
comment = Comment(item['_id'], topicid)
comment.update_comment_label(label)
comment.update_comment_weight(item['weight'])
# 情绪计算
for r in inputs:
if r['subob_rub_neu_label'] == 2:
sentiment = 0 # 0 中性
elif r['subob_rub_neu_label'] == -1:
sentiment = triple_classifier(r) # 1 高兴、2 愤怒、3 悲伤、0无情感
if sentiment == 0:
sentiment = mid_sentiment_classify(r['text'])
if sentiment == -1:
sentiment = 0 # 中性
comment = Comment(r['_id'], topicid)
comment.update_comment_sentiment(sentiment)
def one_topic_calculation_comments_v4(topicid):
"""对评论进行聚类
"""
from comment_clustering_tfidf_v4 import kmeans, tfidf_v4, text_classify, \
cluster_evaluation, choose_cluster
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
eventcomment.clear_cluster(news_id)
results = eventcomment.getNewsComments(news_id)
news = News(news_id)
inputs = []
for r in results:
r['title'] = ''
r['content'] = r['content168'].encode('utf-8')
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 情绪计算
for r in inputs:
sentiment = triple_classifier(r)
comment = Comment(r['_id'], topicid)
comment.update_comment_sentiment(sentiment)
MIN_CLUSTERING_INPUT = 50
MIN_CLUSTER_NUM = 2
MAX_CLUSTER_NUM = 15
if len(inputs) >= MIN_CLUSTERING_INPUT:
tfidf_word, input_dict = tfidf_v4(inputs)
results = choose_cluster(tfidf_word, inputs, MIN_CLUSTER_NUM, MAX_CLUSTER_NUM)
# for k, v in results.iteritems():
# print k, len(v)
#评论文本聚类
cluster_text = text_classify(inputs, results, tfidf_word)
evaluation_inputs = []
for k,v in enumerate(cluster_text):
inputs[k]['label'] = v['label']
inputs[k]['weight'] = v['weight']
evaluation_inputs.append(inputs[k])
#簇评价
recommend_text = cluster_evaluation(evaluation_inputs)
for label, items in recommend_text.iteritems():
if label == 'other':
label = news.otherClusterId
if len(items):
eventcomment.save_cluster(label, news_id, int(time.time()))
if label != news.otherClusterId:
fwords = results[label]
eventcomment.update_feature_words(label, fwords)
for item in items:
comment = Comment(item['_id'], topicid)
comment.update_comment_label(label)
comment.update_comment_weight(item['weight'])
def one_topic_calculation_comments_v2(topicid):
"""对评论进行聚类
"""
from comment_clustering_tfidf_v2 import kmeans, tfidf_v2, text_classify, cluster_evaluation, global_text_weight
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
eventcomment.clear_cluster(news_id)
results = eventcomment.getNewsComments(news_id)
news = News(news_id)
inputs = []
for r in results:
r['title'] = ''
r['content'] = r['content168'].encode('utf-8')
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 情绪计算
for r in inputs:
sentiment = triple_classifier(r)
comment = Comment(r['_id'], topicid)
comment.update_comment_sentiment(sentiment)
tfidf_word = tfidf_v2(inputs)
#聚类个数=过滤后文本数/2向上取整，大于10的取10
kmeans_cluster_number = int(math.ceil(float(len(inputs)) / 5.0))
if kmeans_cluster_number > 10:
kmeans_cluster_number = 10
if kmeans_cluster_number < 5:
kmeans_cluster_number = 5
# 评论词聚类
word_label = kmeans(tfidf_word, inputs, k=kmeans_cluster_number)
# 计算全局文本权重
for r in inputs:
gweight = global_text_weight(r['content'], tfidf_word)
comment = Comment(r['_id'], topicid)
comment.update_comment_global_weight(gweight)
# 评论文本分类
results = text_classify(inputs, word_label, tfidf_word)
#簇评价
reserved_num = int(math.ceil(float(kmeans_cluster_number) / 2.0))
LEAST_CLUSTER_SIZE = 3 # 最小的簇大小
TOPK_FREQ = 10
TOPK_WEIGHT = 5
LEAST_FREQ = 0
final_cluster_results = cluster_evaluation(results, top_num=reserved_num, topk_freq=TOPK_FREQ, \
least_freq=LEAST_FREQ, least_size=LEAST_CLUSTER_SIZE, topk_weight=TOPK_WEIGHT)
for label, items in final_cluster_results.iteritems():
if label == 'other':
label = news.otherClusterId
if len(items):
eventcomment.save_cluster(label, news_id, int(time.time()))
if label != news.otherClusterId:
fwords = word_label[label]
eventcomment.update_feature_words(label, fwords)
for item in items:
comment = Comment(item['_id'], topicid)
comment.update_comment_label(label)
comment.update_comment_weight(item['weight'])
def one_topic_calculation_comments_v7(topicid):
"""对评论进行聚类
"""
from comment_clustering_tfidf_v7 import tfidf_v2, text_classify, \
cluster_evaluation, choose_cluster
from weibo_subob_rub_neu_classifier import weibo_subob_rub_neu_classifier
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
eventcomment.clear_cluster(news_id)
results = eventcomment.getNewsComments(news_id)
news = News(news_id)
# 数据字段预处理
inputs = []
for r in results:
r['title'] = ''
r['content168'] = r['content168'].encode('utf-8')
r['content'] = r['content168']
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 去除垃圾和新闻文本
items = weibo_subob_rub_neu_classifier(inputs)
inputs = []
for item in items:
subob_rub_neu_label = item['subob_rub_neu_label']
if not subob_rub_neu_label in [1, 0]:
# 1表示垃圾文本，0表示新闻文本
inputs.append(item)
MIN_CLUSTERING_INPUT = 30
MIN_CLUSTER_NUM = 2
MAX_CLUSTER_NUM = 10
# TFIDF词、聚类数量自动选择、vsm作属性也要可设成参数
if len(inputs) >= MIN_CLUSTERING_INPUT:
tfidf_word, input_dict = tfidf_v2(inputs)
results = choose_cluster(tfidf_word, inputs, MIN_CLUSTER_NUM,
MAX_CLUSTER_NUM)
#评论文本聚类
cluster_text = text_classify(inputs, results, tfidf_word)
evaluation_inputs = []
for k, v in enumerate(cluster_text):
inputs[k]['label'] = v['label']
inputs[k]['weight'] = v['weight']
evaluation_inputs.append(inputs[k])
#簇评价
recommend_text = cluster_evaluation(evaluation_inputs)
for label, items in recommend_text.iteritems():
if label == 'other':
label = news.otherClusterId
if len(items):
eventcomment.save_cluster(label, news_id, int(time.time()))
if label != news.otherClusterId:
fwords = results[label]
eventcomment.update_feature_words(label, fwords)
for item in items:
comment = Comment(item['_id'], topicid)
comment.update_comment_label(label)
comment.update_comment_weight(item['weight'])
# 情绪计算
for r in inputs:
if r['subob_rub_neu_label'] == 2:
sentiment = 0 # 0 中性
elif r['subob_rub_neu_label'] == -1:
sentiment = triple_classifier(r) # 1 高兴、2 愤怒、3 悲伤、0无情感
if sentiment == 0:
sentiment = mid_sentiment_classify(r['text'])
if sentiment == -1:
sentiment = 0 # 中性
comment = Comment(r['_id'], topicid)
comment.update_comment_sentiment(sentiment)
def one_topic_calculation_comments_v4(topicid):
"""对评论进行聚类
"""
from comment_clustering_tfidf_v4 import kmeans, tfidf_v4, text_classify, \
cluster_evaluation, choose_cluster
eventcomment = EventComments(topicid)
newsIds = eventcomment.getNewsIds()
for news_id in newsIds:
eventcomment.clear_cluster(news_id)
results = eventcomment.getNewsComments(news_id)
news = News(news_id)
inputs = []
for r in results:
r['title'] = ''
r['content'] = r['content168'].encode('utf-8')
r['text'] = r['content168']
item = ad_filter(r)
if item['ad_label'] == 0:
inputs.append(item)
# 情绪计算
for r in inputs:
sentiment = triple_classifier(r)
comment = Comment(r['_id'], topicid)
comment.update_comment_sentiment(sentiment)
MIN_CLUSTERING_INPUT = 50
MIN_CLUSTER_NUM = 2
MAX_CLUSTER_NUM = 15
if len(inputs) >= MIN_CLUSTERING_INPUT:
tfidf_word, input_dict = tfidf_v4(inputs)
results = choose_cluster(tfidf_word, inputs, MIN_CLUSTER_NUM,
MAX_CLUSTER_NUM)
# for k, v in results.iteritems():
# print k, len(v)
#评论文本聚类
cluster_text = text_classify(inputs, results, tfidf_word)
evaluation_inputs = []
for k, v in enumerate(cluster_text):
inputs[k]['label'] = v['label']
inputs[k]['weight'] = v['weight']
evaluation_inputs.append(inputs[k])
#簇评价
recommend_text = cluster_evaluation(evaluation_inputs)
for label, items in recommend_text.iteritems():
if label == 'other':
label = news.otherClusterId
if len(items):
eventcomment.save_cluster(label, news_id, int(time.time()))
if label != news.otherClusterId:
fwords = results[label]
eventcomment.update_feature_words(label, fwords)
for item in items:
comment = Comment(item['_id'], topicid)
comment.update_comment_label(label)
comment.update_comment_weight(item['weight'])
