def weibo_subob_rub_neu_classifier(items, batch=RUBBISH_BATCH_COUNT): ''' 分类主函数: 输入数据:weibo(list元素),示例:[[mid,text,...],[mid,text,...]...] batch: rubbish filter的参数 输出数据:label_data(字典元素),示例:{{'mid':类别标签},{'mid':类别标签}...} 1表示垃圾文本,0表示新闻文本,[2表示中性文本, 已去除],-1表示有极性的文本 ''' results = [] items = rubbish_classifier(items, batch=batch) for item in items: label = 1 if item['rub_label'] == 1: label = 1 # 垃圾 else: item = subob_classifier(item) if item['subob_label'] == 1: label = 0 # 客观 else: sentiment = triple_classifier(item) if sentiment == 0: # label = 2 # 中性 label = cut_mid_weibo(item['content168']) else: label = -1 # 有极性 item['subob_rub_neu_label'] = label results.append(item) return results
def comments_rubbish_clustering_calculation(comments, logger, cluster_num=COMMENT_WORDS_CLUSTER_NUM, \ cluster_eva_min_size=CLUSTER_EVA_MIN_SIZE, \ version=COMMENT_CLUSTERING_PROCESS_FOR_CLUTO_VERSION): """评论垃圾过滤、聚类 input: comments comment中包含news_id, news_content cluster_infos: 聚簇信息 item_infos:单条信息列表, 数据字段:clusterid、weight、same_from、duplicate """ # 无意义信息的clusterid,包括ad_filter分出来的广告,svm分出的垃圾,主客观分类器分出的新闻 NON_CLUSTER_ID = 'nonsense' # 其他类的clusterid OTHER_CLUSTER_ID = 'other' # 最小聚类输入信息条数,少于则不聚类 MIN_CLUSTERING_INPUT = 30 # 簇信息,主要是簇的特征词信息 clusters_infos = {'features': dict()} # 去除sentiment label clusterid ad_label subob_label rub_label clear_keys = ['label', 'ad_label', 'subob_label', 'rub_label', 'weight'] inputs = [] for r in comments: for key in clear_keys: if key in r: del r[key] inputs.append(r) comments = inputs # 单条信息list,每条信息存储 clusterid weight sentiment字段 items_infos = [] # 数据字段预处理 inputs = [] for r in comments: r['title'] = '' try: r['content168'] = r['content168'].encode('utf-8') except: r['content168'] = r['text'].encode('utf-8') r['content'] = r['content168'] r['text'] = r['content168'] if 'news_content' in r and r['news_content']: r['news_content'] = r['news_content'].encode('utf-8') else: r['news_content'] = '' # 简单规则过滤广告 item = ad_filter(r) if item['ad_label'] == 0: inputs.append(item) else: item['clusterid'] = NON_CLUSTER_ID + '_rub' items_infos.append(item) # svm去除垃圾 items = rubbish_classifier(inputs) inputs = [] for item in items: if item['rub_label'] == 1: item['clusterid'] = NON_CLUSTER_ID + '_rub' items_infos.append(item) else: inputs.append(item) # 按新闻对评论归类 results = comment_news(inputs) final_inputs = [] for news_id, _inputs in results.iteritems(): # 结合新闻,过滤评论 _inputs = filter_comment(_inputs) inputs = [r for r in _inputs if r['rub_label'] == 0] inputs_rubbish = [r for r in _inputs if r['rub_label'] == 1] for r in inputs_rubbish: r['clusterid'] = NON_CLUSTER_ID + '_rub' items_infos.append(r) if len(inputs) >= MIN_CLUSTERING_INPUT: tfidf_word, input_dict = tfidf_v2(inputs) results = choose_cluster(tfidf_word, inputs, \ cluster_num=cluster_num, version=version) #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, 权重及簇标签 recommend_text = cluster_evaluation(evaluation_inputs, min_size=cluster_eva_min_size) for label, items in recommend_text.iteritems(): if label != OTHER_CLUSTER_ID: clusters_infos['features'][label] = results[label] print '11111',results[label] for item in items: item['clusterid'] = label item['weight'] = item['weight'] final_inputs.extend(items) else: for item in items: item['clusterid'] = OTHER_CLUSTER_ID items_infos.extend(items) else: # 如果信息条数小于,则直接展示信息列表 tfidf_word, input_dict = tfidf_v2(inputs) uuid_label = str(uuid.uuid4()) clusters_infos['features'][uuid_label] = [kw for kw, count in tfidf_word] print '22222222',clusters_infos['features'][uuid_label] for r in inputs: r['clusterid'] = uuid_label r['weight'] = global_weight_cal_tfidf(tfidf_word, r) final_inputs.extend(inputs) # 去重,根据子观点类别去重 cluster_items = dict() for r in final_inputs: clusterid = r['clusterid'] try: cluster_items[clusterid].append(r) except KeyError: cluster_items[clusterid] = [r] for clusterid, items in cluster_items.iteritems(): results = duplicate(items) items_infos.extend(results) return {'cluster_infos': clusters_infos, 'item_infos': items_infos}
def comments_sentiment_rubbish_calculation(comments, logger): """输入为一堆comments, 字段包括title、content168 输出: item_infos:单条信息列表, 数据字段:sentiment、same_from、duplicate """ # 无意义信息的clusterid,包括ad_filter分出来的广告,svm分出的垃圾,主客观分类器分出的新闻 NON_CLUSTER_ID = 'nonsense' # 有意义的信息clusterid MEAN_CLUSTER_ID = 'sentiment' # 单条信息list,每条信息存储 clusterid weight sentiment字段 items_infos = [] # 去除sentiment label clusterid ad_label subob_label rub_label clear_keys = ['sentiment', 'label', 'clusterid', 'ad_label', 'subob_label', 'rub_label', 'weight'] inputs = [] for r in comments: for key in clear_keys: if key in r: del r[key] inputs.append(r) comments = inputs # 数据字段预处理 inputs = [] for r in comments: r['title'] = '' try: r['content168'] = r['content168'].encode('utf-8') except: r['content168'] = r['text'].encode('utf-8') r['content'] = r['content168'] r['text'] = r['content168'] inputs.append(r) # 先分中性及3类分类器 svm_inputs = [] for r in inputs: sentiment = neutral_classifier(r) if sentiment != 0: sentiment = triple_classifier(r) if sentiment == 0: svm_inputs.append(r) else: r['sentiment'] = sentiment items_infos.append(r) else: svm_inputs.append(r) # 情绪调整 senti_modify_inputs = [] for r in svm_inputs: sentiment = mid_sentiment_classify(r['text']) if sentiment == -1: sentiment = 0 # 中性 if sentiment != 0: r['sentiment'] = sentiment items_infos.append(r) else: r['sentiment'] = sentiment senti_modify_inputs.append(r) # 新闻分类 inputs = [] for r in senti_modify_inputs: r = subob_classifier(r) if r['subob_label'] == 1: # 主客观文本分类 r['sentiment'] = NON_CLUSTER_ID + '_news' # 新闻 items_infos.append(r) else: inputs.append(r) # 去垃圾 items = rubbish_classifier(inputs) for item in items: if item['rub_label'] == 1: # svm去垃圾 item['sentiment'] = NON_CLUSTER_ID + '_rub' else: # 简单规则过滤广告 item = ad_filter(item) if item['ad_label'] == 1: item['sentiment'] = NON_CLUSTER_ID + '_rub' items_infos.append(item) # 去重,在一个情绪类别下将文本去重 sentiment_dict = dict() for item in items_infos: if 'sentiment' in item: sentiment = item['sentiment'] try: sentiment_dict[sentiment].append(item) except KeyError: sentiment_dict[sentiment] = [item] items_infos = [] for sentiment, items in sentiment_dict.iteritems(): items_list = duplicate(items) items_infos.extend(items_list) return {'item_infos': items_infos}
def comments_rubbish_clustering_calculation(comments, logger, cluster_num=COMMENT_WORDS_CLUSTER_NUM, \ cluster_eva_min_size=CLUSTER_EVA_MIN_SIZE, \ version=COMMENT_CLUSTERING_PROCESS_FOR_CLUTO_VERSION): """评论垃圾过滤、聚类 input: comments comment中包含news_id, news_content cluster_infos: 聚簇信息 item_infos:单条信息列表, 数据字段:clusterid、weight、same_from、duplicate """ # 无意义信息的clusterid,包括ad_filter分出来的广告,svm分出的垃圾,主客观分类器分出的新闻 NON_CLUSTER_ID = 'nonsense' # 其他类的clusterid OTHER_CLUSTER_ID = 'other' # 最小聚类输入信息条数,少于则不聚类 MIN_CLUSTERING_INPUT = 30 # 簇信息,主要是簇的特征词信息 clusters_infos = {'features': dict()} # 去除sentiment label clusterid ad_label subob_label rub_label clear_keys = ['label', 'ad_label', 'subob_label', 'rub_label', 'weight'] inputs = [] for r in comments: for key in clear_keys: if key in r: del r[key] inputs.append(r) comments = inputs # 单条信息list,每条信息存储 clusterid weight sentiment字段 items_infos = [] # 数据字段预处理 inputs = [] for r in comments: r['title'] = '' r['content168'] = r['content168'].encode('utf-8') r['content'] = r['content168'] r['text'] = r['content168'] if 'news_content' in r and r['news_content']: r['news_content'] = r['news_content'].encode('utf-8') else: r['news_content'] = '' # 简单规则过滤广告 item = ad_filter(r) if item['ad_label'] == 0: inputs.append(item) else: item['clusterid'] = NON_CLUSTER_ID + '_rub' items_infos.append(item) # svm去除垃圾 items = rubbish_classifier(inputs) inputs = [] for item in items: if item['rub_label'] == 1: item['clusterid'] = NON_CLUSTER_ID + '_rub' items_infos.append(item) else: inputs.append(item) # 按新闻对评论归类 results = comment_news(inputs) final_inputs = [] for news_id, _inputs in results.iteritems(): # 结合新闻,过滤评论 _inputs = filter_comment(_inputs) inputs = [r for r in _inputs if r['rub_label'] == 0] inputs_rubbish = [r for r in _inputs if r['rub_label'] == 1] for r in inputs_rubbish: r['clusterid'] = NON_CLUSTER_ID + '_rub' items_infos.append(r) if len(inputs) >= MIN_CLUSTERING_INPUT: tfidf_word, input_dict = tfidf_v2(inputs) results = choose_cluster(tfidf_word, inputs, \ cluster_num=cluster_num, version=version) #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, 权重及簇标签 recommend_text = cluster_evaluation(evaluation_inputs, min_size=cluster_eva_min_size) for label, items in recommend_text.iteritems(): if label != OTHER_CLUSTER_ID: clusters_infos['features'][label] = results[label] for item in items: item['clusterid'] = label item['weight'] = item['weight'] final_inputs.extend(items) else: for item in items: item['clusterid'] = OTHER_CLUSTER_ID items_infos.extend(items) else: # 如果信息条数小于,则直接展示信息列表 tfidf_word, input_dict = tfidf_v2(inputs) uuid_label = str(uuid.uuid4()) clusters_infos['features'][uuid_label] = [kw for kw, count in tfidf_word] for r in inputs: r['clusterid'] = uuid_label r['weight'] = global_weight_cal_tfidf(tfidf_word, r) final_inputs.extend(inputs) # 去重,根据子观点类别去重 cluster_items = dict() for r in final_inputs: clusterid = r['clusterid'] try: cluster_items[clusterid].append(r) except KeyError: cluster_items[clusterid] = [r] for clusterid, items in cluster_items.iteritems(): results = duplicate(items) items_infos.extend(results) return {'cluster_infos': clusters_infos, 'item_infos': items_infos}
def comments_sentiment_rubbish_calculation(comments, logger): """输入为一堆comments, 字段包括title、content168 输出: item_infos:单条信息列表, 数据字段:sentiment、same_from、duplicate """ # 无意义信息的clusterid,包括ad_filter分出来的广告,svm分出的垃圾,主客观分类器分出的新闻 NON_CLUSTER_ID = 'nonsense' # 有意义的信息clusterid MEAN_CLUSTER_ID = 'sentiment' # 单条信息list,每条信息存储 clusterid weight sentiment字段 items_infos = [] # 去除sentiment label clusterid ad_label subob_label rub_label clear_keys = ['sentiment', 'label', 'clusterid', 'ad_label', 'subob_label', 'rub_label', 'weight'] inputs = [] for r in comments: for key in clear_keys: if key in r: del r[key] inputs.append(r) comments = inputs # 数据字段预处理 inputs = [] for r in comments: r['title'] = '' r['content168'] = r['content168'].encode('utf-8') r['content'] = r['content168'] r['text'] = r['content168'] inputs.append(r) # 先分中性及3类分类器 svm_inputs = [] for r in inputs: sentiment = neutral_classifier(r) if sentiment != 0: sentiment = triple_classifier(r) if sentiment == 0: svm_inputs.append(r) else: r['sentiment'] = sentiment items_infos.append(r) else: svm_inputs.append(r) # 情绪调整 senti_modify_inputs = [] for r in svm_inputs: sentiment = mid_sentiment_classify(r['text']) if sentiment == -1: sentiment = 0 # 中性 if sentiment != 0: r['sentiment'] = sentiment items_infos.append(r) else: r['sentiment'] = sentiment senti_modify_inputs.append(r) # 新闻分类 inputs = [] for r in senti_modify_inputs: r = subob_classifier(r) if r['subob_label'] == 1: # 主客观文本分类 r['sentiment'] = NON_CLUSTER_ID + '_news' # 新闻 items_infos.append(r) else: inputs.append(r) # 去垃圾 items = rubbish_classifier(inputs) for item in items: if item['rub_label'] == 1: # svm去垃圾 item['sentiment'] = NON_CLUSTER_ID + '_rub' else: # 简单规则过滤广告 item = ad_filter(item) if item['ad_label'] == 1: item['sentiment'] = NON_CLUSTER_ID + '_rub' items_infos.append(item) # 去重,在一个情绪类别下将文本去重 sentiment_dict = dict() for item in items_infos: if 'sentiment' in item: sentiment = item['sentiment'] try: sentiment_dict[sentiment].append(item) except KeyError: sentiment_dict[sentiment] = [item] items_infos = [] for sentiment, items in sentiment_dict.iteritems(): items_list = duplicate(items) items_infos.extend(items_list) return {'item_infos': items_infos}