def title_cluster(df, save_df=False): """按新闻标题聚类""" df_title = df.copy() df_title = title_preprocess(df_title) word_library_list = counter.get_word_library(df_title['title_cut']) single_frequency_words_list = counter.get_single_frequency_words(df_title['title_cut']) max_features = len(word_library_list) - len(single_frequency_words_list) // 2 title_matrix = modeling.feature_extraction(df_title['title_'], vectorizer='CountVectorizer', vec_args={'max_df': 1.0, 'min_df': 1, 'max_features': max_features}) title_dbscan = modeling.get_cluster(title_matrix, cluster='DBSCAN', cluster_args={'eps': 0.4, 'min_samples': 4, 'metric': 'cosine'}) title_labels = modeling.get_labels(title_dbscan) df_title['title_label'] = title_labels df_non_outliers = modeling.get_non_outliers_data(df_title, label_column='title_label') title_label_num = counter.get_num_of_value_no_repeat(df_non_outliers['title_label'].tolist()) print('按新闻标题聚类,一共有%d个簇(不包括离群点)' % title_label_num) title_rank = modeling.label2rank(title_labels) df_title['title_rank'] = title_rank for i in range(1, title_label_num + 1): df_ = df_title[df_title['title_rank'] == i] title_top_list = counter.get_most_common_words(df_['title_cut'], top_n=10) print(title_top_list) if save_df: df_title.drop(['content', 'title_', 'title_label'], axis=1, inplace=True) news_crawler.save_news(df_title, os.path.join(results_path, 'df_title_rank.csv')) return df_title
def content_cluster(df, df_save=False): """按新闻内容聚类""" df_content = df.copy() df_content = content_preprocess(df_content) word_library_list = counter.get_word_library(df_content['content_cut']) single_frequency_words_list = counter.get_single_frequency_words(df_content['content_cut']) max_features = len(word_library_list) - len(single_frequency_words_list) // 2 content_matrix = modeling.feature_extraction(df_content['content_'], vectorizer='CountVectorizer', vec_args={'max_df': 0.95, 'min_df': 1, 'max_features': max_features}) content_dbscan = modeling.get_cluster(content_matrix, cluster='DBSCAN', cluster_args={'eps': 0.35, 'min_samples': 4, 'metric': 'cosine'}) content_labels = modeling.get_labels(content_dbscan) df_content['content_label'] = content_labels df_non_outliers = modeling.get_non_outliers_data(df_content, label_column='content_label') content_label_num = counter.get_num_of_value_no_repeat(df_non_outliers['content_label'].tolist()) print('按新闻内容聚类,一共有%d个簇(不包括离群点)' % content_label_num) content_rank = modeling.label2rank(content_labels) df_content['content_rank'] = content_rank for i in range(1, content_label_num + 1): df_ = df_content[df_content['content_rank'] == i] content_top_list = counter.get_most_common_words(df_['content_cut'], top_n=15, min_frequency=1) print(content_top_list) if df_save: df_content.drop(['content_', 'content_label'], axis=1, inplace=True) news_crawler.save_news(df_content, os.path.join(results_path, 'df_content_rank.csv')) return df_content
def cluster_word(): n_clusters = Entry_N_Clusters.get() if n_clusters == '': messagebox.showinfo('Message', '请输入词汇聚类的类别数!') return n_clusters = int(n_clusters) top_words_list = counter.get_most_common_words( df_rank_i['content_cut'], top_n=5000, min_frequency=1) model = news_pandas.load_element( os.path.join(models_path, 'word2vec_model.pkl')) word_list, wordvec_list = modeling.get_word_and_wordvec( model, top_words_list) kmeans = modeling.get_cluster(wordvec_list, cluster='KMeans', cluster_args={ 'n_clusters': n_clusters, 'random_state': 9 }) word_label = kmeans.labels_ word_df = pd.DataFrame() word_df['word'] = word_list word_df['wordvec'] = wordvec_list word_df['word_label'] = word_label news_pandas.save_news(word_df, os.path.join(results_path, 'word_df.csv')) messagebox.showinfo('Message', '词汇聚类完成!')
def show_hot_barh(): try: df_non_outliers = news_pandas.load_news( os.path.join(results_path, 'news_non_outliers.csv')) df_non_outliers['content_cut'] = df_non_outliers['content_cut'].map( eval) except FileNotFoundError: messagebox.showinfo('Message', '请先对新闻内容文本进行聚类!') return rank_num = counter.get_num_of_value_no_repeat(df_non_outliers['rank']) value = [ df_non_outliers[df_non_outliers['rank'] == i].shape[0] for i in range(1, rank_num + 1) ] yticks1 = [ str( counter.get_most_common_words( df_non_outliers[df_non_outliers['rank'] == i]['content_cut'], top_n=10)) + str(i) for i in range(1, rank_num + 1) ] # yticks2 = [modeling.get_key_sentences('\n'.join(df_non_outliers[df_non_outliers['rank'] == i]['title_']), # num=1) for i in range(1, rank_num + 1)] drawing.draw_clustering_analysis_barh(rank_num, value, yticks1, title='热点新闻分布饼图')
def get_key_words(): df_title = news_crawler.load_news( os.path.join(results_path, 'df_title_rank.csv')) df_content = news_crawler.load_news( os.path.join(results_path, 'df_content_rank.csv')) df_title['title_cut'] = df_title['title_cut'].map(eval) df_content['content_cut'] = df_content['content_cut'].map(eval) df_title_content = df_title.copy() df_title_content['content_cut'] = df_content['content_cut'] df_title_content['content_rank'] = df_content['content_rank'] df_title_content = modeling.get_non_outliers_data( df_title_content, label_column='title_rank') title_rank_num = counter.get_num_of_value_no_repeat( (df_title_content['title_rank'])) for i in range(1, title_rank_num + 1): df_i = df_title_content[df_title_content['title_rank'] == i] title = '\n'.join(df_i['title'].tolist()) title = modeling.get_key_sentences(title, num=1) print('热点:', title) content_rank = [k for k in df_i['content_rank']] content_rank = set(content_rank) for j in content_rank: df_j = df_i[df_i['content_rank'] == j] most_commmon_words = counter.get_most_common_words( df_j['content_cut'], top_n=20, min_frequency=5) if len(most_commmon_words) > 0: print('相关词汇:', most_commmon_words)
def show_hot_words_details(): top_words_list = counter.get_most_common_words(df_rank_i['content_cut'], top_n=5000, min_frequency=1) top_words = '\n'.join(top_words_list) news_pandas.save_text(top_words, os.path.join(texts_path, 'top_words.txt')) os.system(editor + ' ' + os.path.join(texts_path, 'top_words.txt') + ' &')
def show_wordlib(): try: document_segment = news_pandas.load_text(os.path.join(texts_path, 'document_segment.txt')) except FileNotFoundError: messagebox.showinfo('Message', '没有分词后的文件!') return words = document_segment.split() word_library = counter.get_most_common_words(words) word_library = [word for word in word_library if re.match(r'^[0-9A-Za-z\u4E00-\u9FFF]+$', word)] word_library = '\n'.join(word_library) news_pandas.save_text(word_library, os.path.join(texts_path, 'word_library.txt')) filename = os.path.join(texts_path, 'word_library.txt') editor(filename)