每个文本文件包含相对应类的数据（0：喜悦1：愤怒2：厌恶3：低落对应不同类别的感情)

1. 文本读取
2. 用均值的方差，在高斯分布里面计算某个词的概率。
3. 对文本特征进行提取，提取词频。
4. 通过词频在各种词频目录里面进行匹配。
5. 对模型的准确率的预测。

main.py

1 # -*- coding: utf-8 -*-  2 import os  3 import pandas as pd  4 import nltk  5 from tools import proc_text, split_train_test, get_word_list_from_data, \  6     extract_feat_from_data, cal_acc  7 from nltk.text import TextCollection  8 from sklearn.naive_bayes import GaussianNB  9  10 dataset_path = './dataset' 11 text_filenames = ['0_simplifyweibo.txt', '1_simplifyweibo.txt', 12                   '2_simplifyweibo.txt', '3_simplifyweibo.txt'] 13  14 # 原始数据的csv文件 15 output_text_filename = 'raw_weibo_text.csv' 16  17 # 清洗好的文本数据文件 18 output_cln_text_filename = 'clean_weibo_text.csv' 19  20 # 处理和清洗文本数据的时间较长，通过设置is_first_run进行配置 21 # 如果是第一次运行需要对原始文本数据进行处理和清洗，需要设为True 22 # 如果之前已经处理了文本数据，并已经保存了清洗好的文本数据，设为False即可 23 is_first_run = True 24  25  26 def read_and_save_to_csv(): 27     """ 28         读取原始文本数据，将标签和文本数据保存成csv 29     """ 30  31     text_w_label_df_lst = [] 32     for text_filename in text_filenames: 33         text_file = os.path.join(dataset_path, text_filename) 34  35         # 获取标签，即0, 1, 2, 3 36         label = int(text_filename[0]) 37  38         # 读取文本文件 39         with open(text_file, 'r', encoding='utf-8') as f: 40             lines = f.read().splitlines() 41  42         labels = [label] * len(lines) 43  44         text_series = pd.Series(lines) 45         label_series = pd.Series(labels) 46  47         # 构造dataframe 48         text_w_label_df = pd.concat([label_series, text_series], axis=1) 49         text_w_label_df_lst.append(text_w_label_df) 50  51     result_df = pd.concat(text_w_label_df_lst, axis=0) 52  53     # 保存成csv文件 54     result_df.columns = ['label', 'text'] 55     result_df.to_csv(os.path.join(dataset_path, output_text_filename), 56                      index=None, encoding='utf-8') 57  58  59 def run_main(): 60     """ 61         主函数 62     """ 63     # 1. 数据读取，处理，清洗，准备 64     if is_first_run: 65         print('处理清洗文本数据中...', end=' ') 66         # 如果是第一次运行需要对原始文本数据进行处理和清洗 67  68         # 读取原始文本数据，将标签和文本数据保存成csv 69         read_and_save_to_csv() 70  71         # 读取处理好的csv文件，构造数据集 72         text_df = pd.read_csv(os.path.join(dataset_path, output_text_filename), 73                               encoding='utf-8') 74  75         # 处理文本数据 76         text_df['text'] = text_df['text'].apply(proc_text) 77  78         # 过滤空字符串 79         text_df = text_df[text_df['text'] != ''] 80  81         # 保存处理好的文本数据 82         text_df.to_csv(os.path.join(dataset_path, output_cln_text_filename), 83                        index=None, encoding='utf-8') 84         print('完成，并保存结果。') 85  86     # 2. 分割训练集、测试集 87     print('加载处理好的文本数据') 88     clean_text_df = pd.read_csv(os.path.join(dataset_path, output_cln_text_filename), 89                                 encoding='utf-8') 90     # 分割训练集和测试集 91     train_text_df, test_text_df = split_train_test(clean_text_df) 92     # 查看训练集测试集基本信息 93     print('训练集中各类的数据个数：', train_text_df.groupby('label').size()) 94     print('测试集中各类的数据个数：', test_text_df.groupby('label').size()) 95  96     # 3. 特征提取 97     # 计算词频 98     n_common_words = 200 99 100     # 将训练集中的单词拿出来统计词频101     print('统计词频...')102     all_words_in_train = get_word_list_from_data(train_text_df)103     fdisk = nltk.FreqDist(all_words_in_train)104     common_words_freqs = fdisk.most_common(n_common_words)105     print('出现最多的{}个词是：'.format(n_common_words))106     for word, count in common_words_freqs:107         print('{}: {}次'.format(word, count))108     print()109 110     # 在训练集上提取特征111     text_collection = TextCollection(train_text_df['text'].values.tolist())112     print('训练样本提取特征...', end=' ')113     train_X, train_y = extract_feat_from_data(train_text_df, text_collection, common_words_freqs)114     print('完成')115     print()116 117     print('测试样本提取特征...', end=' ')118     test_X, test_y = extract_feat_from_data(test_text_df, text_collection, common_words_freqs)119     print('完成')120 121     # 4. 训练模型Naive Bayes122     print('训练模型...', end=' ')123     gnb = GaussianNB()124     gnb.fit(train_X, train_y)125     print('完成')126     print()127 128     # 5. 预测129     print('测试模型...', end=' ')130     test_pred = gnb.predict(test_X)131     print('完成')132 133     # 输出准确率134     print('准确率：', cal_acc(test_y, test_pred))135 136 if __name__ == '__main__':137     run_main()

tools.py

1 # -*- coding: utf-8 -*-  2 import re  3 import jieba.posseg as pseg  4 import pandas as pd  5 import math  6 import numpy as np  7   8 # 加载常用停用词  9 stopwords1 = [line.rstrip() for line in open('./中文停用词库.txt', 'r', encoding='utf-8')] 10 # stopwords2 = [line.rstrip() for line in open('./哈工大停用词表.txt', 'r', encoding='utf-8')] 11 # stopwords3 = [line.rstrip() for line in open('./四川大学机器智能实验室停用词库.txt', 'r', encoding='utf-8')] 12 # stopwords = stopwords1 + stopwords2 + stopwords3 13 stopwords = stopwords1 14  15  16 def proc_text(raw_line): 17     """ 18         处理每行的文本数据 19         返回分词结果 20     """ 21     # 1. 使用正则表达式去除非中文字符 22     filter_pattern = re.compile('[^\u4E00-\u9FD5]+') 23     chinese_only = filter_pattern.sub('', raw_line) 24  25     # 2. 结巴分词+词性标注 26     words_lst = pseg.cut(chinese_only) 27  28     # 3. 去除停用词 29     meaninful_words = [] 30     for word, flag in words_lst: 31         # if (word not in stopwords) and (flag == 'v'): 32             # 也可根据词性去除非动词等 33         if word not in stopwords: 34             meaninful_words.append(word) 35  36     return ' '.join(meaninful_words) 37  38  39 def split_train_test(text_df, size=0.8): 40     """ 41         分割训练集和测试集 42     """ 43     # 为保证每个类中的数据能在训练集中和测试集中的比例相同，所以需要依次对每个类进行处理 44     train_text_df = pd.DataFrame() 45     test_text_df = pd.DataFrame() 46  47     labels = [0, 1, 2, 3] 48     for label in labels: 49         # 找出label的记录 50         text_df_w_label = text_df[text_df['label'] == label] 51         # 重新设置索引，保证每个类的记录是从0开始索引，方便之后的拆分 52         text_df_w_label = text_df_w_label.reset_index() 53  54         # 默认按80%训练集，20%测试集分割 55         # 这里为了简化操作，取前80%放到训练集中，后20%放到测试集中 56         # 当然也可以随机拆分80%，20%（尝试实现下DataFrame中的随机拆分） 57  58         # 该类数据的行数 59         n_lines = text_df_w_label.shape[0] 60         split_line_no = math.floor(n_lines * size) 61         text_df_w_label_train = text_df_w_label.iloc[:split_line_no, :] 62         text_df_w_label_test = text_df_w_label.iloc[split_line_no:, :] 63  64         # 放入整体训练集，测试集中 65         train_text_df = train_text_df.append(text_df_w_label_train) 66         test_text_df = test_text_df.append(text_df_w_label_test) 67  68     train_text_df = train_text_df.reset_index() 69     test_text_df = test_text_df.reset_index() 70     return train_text_df, test_text_df 71  72  73 def get_word_list_from_data(text_df): 74     """ 75         将数据集中的单词放入到一个列表中 76     """ 77     word_list = [] 78     for _, r_data in text_df.iterrows(): 79         word_list += r_data['text'].split(' ') 80     return word_list 81  82  83 def extract_feat_from_data(text_df, text_collection, common_words_freqs): 84     """ 85         特征提取 86     """ 87     # 这里只选择TF-IDF特征作为例子 88     # 可考虑使用词频或其他文本特征作为额外的特征 89  90     n_sample = text_df.shape[0] 91     n_feat = len(common_words_freqs) 92     common_words = [word for word, _ in common_words_freqs] 93  94     # 初始化 95     X = np.zeros([n_sample, n_feat]) 96     y = np.zeros(n_sample) 97  98     print('提取特征...') 99     for i, r_data in text_df.iterrows():100         if (i + 1) % 5000 == 0:101             print('已完成{}个样本的特征提取'.format(i + 1))102 103         text = r_data['text']104 105         feat_vec = []106         for word in common_words:107             if word in text:108                 # 如果在高频词中，计算TF-IDF值109                 tf_idf_val = text_collection.tf_idf(word, text)110             else:111                 tf_idf_val = 0112 113             feat_vec.append(tf_idf_val)114 115         # 赋值116         X[i, :] = np.array(feat_vec)117         y[i] = int(r_data['label'])118 119     return X, y120 121 122 def cal_acc(true_labels, pred_labels):123     """124         计算准确率125     """126     n_total = len(true_labels)127     correct_list = [true_labels[i] == pred_labels[i] for i in range(n_total)]128 129     acc = sum(correct_list) / n_total130     return acc