LDA利用python进行主题分析提取

淡淡的烟草味﹌ 2022-04-03 06:58 837阅读 0赞

数据科学老师布置任务，使用LDA写一个针对网页的主题提取实验。下面我把代码贴上，把所需要的文件传上。

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    from gensim import corpora, models, similarities
    from pprint import pprint
    import time
    
    # import logging
    # logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
    
    
    def load_stopword():
        '''
        加载停用词表
        :return: 返回停用词的列表
        '''
        f_stop = open('/home/zlee/PycharmProjects/multi-project/16.LDA_test.txt')
        sw = [line.strip() for line in f_stop]
        f_stop.close()
        return sw
    
    
    if __name__ == '__main__':
    
        print('1.初始化停止词列表 ------')
        # 开始的时间
        t_start = time.time()
        # 加载停用词表
        stop_words = load_stopword()
    
        print('2.开始读入语料数据 ------ ')
        # 读入语料库
        f = open('/home/zlee/PycharmProjects/multi-project/22.news.dat')
        # 语料库分词并去停用词
        texts = [[word for word in line.strip().lower().split() if word not in stop_words] for line in f]
    
        print('读入语料数据完成，用时%.3f秒' % (time.time() - t_start))
        f.close()
        M = len(texts)
        print('文本数目：%d个' % M)
    
        print('3.正在建立词典 ------')
        # 建立字典
        dictionary = corpora.Dictionary(texts)
        V = len(dictionary)
    
        print ('4.正在计算文本向量 ------')
        # 转换文本数据为索引，并计数
        corpus = [dictionary.doc2bow(text) for text in texts]
    
        print ('5.正在计算文档TF-IDF ------')
        t_start = time.time()
        # 计算tf-idf值
        corpus_tfidf = models.TfidfModel(corpus)[corpus]
        print ('建立文档TF-IDF完成，用时%.3f秒' % (time.time() - t_start))
    
        print ('6.LDA模型拟合推断 ------')
        # 训练模型
        num_topics = 30
        t_start = time.time()
        lda = models.LdaModel(corpus_tfidf, num_topics=num_topics, id2word=dictionary,
                                alpha=0.01, eta=0.01, minimum_probability=0.001,
                                update_every = 1, chunksize = 100, passes = 1)
        print('LDA模型完成，训练时间为\t%.3f秒' % (time.time() - t_start))
    
        # 随机打印某10个文档的主题
        num_show_topic = 10  # 每个文档显示前几个主题
        print('7.结果：10个文档的主题分布：--')
        doc_topics = lda.get_document_topics(corpus_tfidf)  # 所有文档的主题分布
        idx = np.arange(M)
        np.random.shuffle(idx)
        idx = idx[:10]
        for i in idx:
            topic = np.array(doc_topics[i])
            topic_distribute = np.array(topic[:, 1])
            # print topic_distribute
            topic_idx = topic_distribute.argsort()[:-num_show_topic-1:-1]
            print ('第%d个文档的前%d个主题：' % (i, num_show_topic)), topic_idx
            print(topic_distribute[topic_idx])
    
        num_show_term = 7   # 每个主题显示几个词
        print('8.结果：每个主题的词分布：--')
        for topic_id in range(num_topics):
            print('主题#%d：\t' % topic_id)
            term_distribute_all = lda.get_topic_terms(topicid=topic_id)
            term_distribute = term_distribute_all[:num_show_term]
            term_distribute = np.array(term_distribute)
            term_id = term_distribute[:, 0].astype(np.int)
            print('词：\t',)
            for t in term_id:
                print(dictionary.id2token[t],)
            print('\n概率：\t', term_distribute[:, 1])

所需文件后续传上。

链接: https://pan.baidu.com/s/1nbwSKVWW5Wi7Jdy0g5xU9Q 提取码: 36bk