【Python数据分析学习笔记Day6】（六）文本数据分析

相关博客

Python文本数据分析——新闻分类任务
https://blog.csdn.net/qq_41251963/article/details/104147862
python机器学习——NLTK及分析文本数据（自然语言处理基础）
https://blog.csdn.net/qq_41251963/article/details/81702821
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https://blog.csdn.net/qq_41251963/article/details/85715952
python机器学习——贝叶斯方法
https://blog.csdn.net/qq_41251963/article/details/81739403

典型的文本预处理流程

NLTK

语料库

import nltk
from nltk.corpus import brown # 需要下载brown语料库
# 引用布朗大学的语料库

# 查看语料库包含的类别
print(brown.categories())

['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']

# 查看brown语料库
print('共有{}个句子'.format(len(brown.sents())))
print('共有{}个单词'.format(len(brown.words())))

共有57340个句子
共有1161192个单词

分词

sentence = "Python is a widely used high-level programming language for general-purpose programming."
tokens = nltk.word_tokenize(sentence) # 需要下载punkt分词模型
print(tokens)

['Python', 'is', 'a', 'widely', 'used', 'high-level', 'programming', 'language', 'for', 'general-purpose', 'programming', '.']

结巴分词

# 安装 pip install jieba
import jieba

seg_list = jieba.cut("我正在学习Python文本数据分析", cut_all=True)
print("全模式: " + "/ ".join(seg_list))  # 全模式

seg_list = jieba.cut("我正在学习Python文本数据分析", cut_all=False)
print("精确模式: " + "/ ".join(seg_list))  # 精确模式

Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\李强\AppData\Local\Temp\jieba.cache
Loading model cost 1.077 seconds.
Prefix dict has been built succesfully.


全模式: 我/ 正在/ 学习/ Python/ 文本/ 本数/ 数据/ 数据分析/ 分析
精确模式: 我/ 正在/ 学习/ Python/ 文本/ 数据分析

词形处理

词干提取(stemming)

# PorterStemmer
from nltk.stem.porter import PorterStemmer

porter_stemmer = PorterStemmer()
print(porter_stemmer.stem('looked'))
print(porter_stemmer.stem('looking'))

look
look

# SnowballStemmer
from nltk.stem import SnowballStemmer

snowball_stemmer = SnowballStemmer('english')
print(snowball_stemmer.stem('looked'))
print(snowball_stemmer.stem('looking'))

look
look

# LancasterStemmer
from nltk.stem.lancaster import LancasterStemmer

lancaster_stemmer = LancasterStemmer()
print(lancaster_stemmer.stem('looked'))
print(lancaster_stemmer.stem('looking'))

look
look

词形归并(lemmatization)

from nltk.stem import WordNetLemmatizer # 需要下载wordnet语料库

wordnet_lematizer = WordNetLemmatizer()
print(wordnet_lematizer.lemmatize('cats'))
print(wordnet_lematizer.lemmatize('boxes'))
print(wordnet_lematizer.lemmatize('are'))
print(wordnet_lematizer.lemmatize('went'))

cat
box
are
went

# 指明词性可以更准确地进行lemma
# lemmatize 默认为名词
print(wordnet_lematizer.lemmatize('are', pos='v'))
print(wordnet_lematizer.lemmatize('went', pos='v'))

be
go

词性标注 (Part-Of-Speech)

import nltk

words = nltk.word_tokenize('Python is a widely used programming language.')
print(nltk.pos_tag(words)) # 需要下载 averaged_perceptron_tagger

[('Python', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('widely', 'RB'), ('used', 'VBN'), ('programming', 'NN'), ('language', 'NN'), ('.', '.')]

去除停用词

from nltk.corpus import stopwords # 需要下载stopwords

filtered_words = [word for word in words if word not in stopwords.words('english')]
print('原始词：', words)
print('去除停用词后：', filtered_words)

原始词： ['Python', 'is', 'a', 'widely', 'used', 'programming', 'language', '.']
去除停用词后： ['Python', 'widely', 'used', 'programming', 'language', '.']

典型的文本预处理流程

import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords

# 原始文本
raw_text = 'Life is like a box of chocolates. You never know what you\'re gonna get.'

# 分词
raw_words = nltk.word_tokenize(raw_text)

# 词形归一化
wordnet_lematizer = WordNetLemmatizer()
words = [wordnet_lematizer.lemmatize(raw_word) for raw_word in raw_words]

# 去除停用词
filtered_words = [word for word in words if word not in stopwords.words('english')]

print('原始文本：', raw_text)
print('预处理结果：', filtered_words)

原始文本： Life is like a box of chocolates. You never know what you're gonna get.
预处理结果： ['Life', 'like', 'box', 'chocolate', '.', 'You', 'never', 'know', "'re", 'gon', 'na', 'get', '.']

情感分析案例

使用机器学习实现

# 简单的例子

import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from nltk.classify import NaiveBayesClassifier

text1 = 'I like the movie so much!'
text2 = 'That is a good movie.'
text3 = 'This is a great one.'
text4 = 'That is a really bad movie.'
text5 = 'This is a terrible movie.'

def proc_text(text):
    """
        预处处理文本
    """
    # 分词
    raw_words = nltk.word_tokenize(text)
    
    # 词形归一化
    wordnet_lematizer = WordNetLemmatizer()    
    words = [wordnet_lematizer.lemmatize(raw_word) for raw_word in raw_words]
    
    # 去除停用词
    filtered_words = [word for word in words if word not in stopwords.words('english')]
    
    # True 表示该词在文本中，为了使用nltk中的分类器
    return {word: True for word in filtered_words}

# 构造训练样本
train_data = [[proc_text(text1), 1],
              [proc_text(text2), 1],
              [proc_text(text3), 1],
              [proc_text(text4), 0],
              [proc_text(text5), 0]]

# 训练模型
nb_model = NaiveBayesClassifier.train(train_data)

# 测试模型
text6 = 'That is a bad one.'
print(nb_model.classify(proc_text(text5)))

0

文本相似度

import nltk
from nltk import FreqDist

text1 = 'I like the movie so much '
text2 = 'That is a good movie '
text3 = 'This is a great one '
text4 = 'That is a really bad movie '
text5 = 'This is a terrible movie'

text = text1 + text2 + text3 + text4 + text5
words = nltk.word_tokenize(text)
freq_dist = FreqDist(words)
print(freq_dist['is'])

4

# 取出常用的n=5个单词
n = 5

# 构造“常用单词列表”
most_common_words = freq_dist.most_common(n)
print(most_common_words)

[('movie', 4), ('is', 4), ('a', 4), ('That', 2), ('This', 2)]

def lookup_pos(most_common_words):
    """
        查找常用单词的位置
    """
    result = {}
    pos = 0
    for word in most_common_words:
        result[word[0]] = pos
        pos += 1
    return result

# 记录位置
std_pos_dict = lookup_pos(most_common_words)
print(std_pos_dict)

{'movie': 0, 'is': 1, 'a': 2, 'That': 3, 'This': 4}

# 新文本
new_text = 'That one is a good movie. This is so good!'

# 初始化向量
freq_vec = [0] * n

# 分词
new_words = nltk.word_tokenize(new_text)

# 在“常用单词列表”上计算词频
for new_word in new_words:
    if new_word in list(std_pos_dict.keys()):
        freq_vec[std_pos_dict[new_word]] += 1

print(freq_vec)

[1, 2, 1, 1, 1]

文本分类

TF-IDF

from nltk.text import TextCollection

text1 = 'I like the movie so much '
text2 = 'That is a good movie '
text3 = 'This is a great one '
text4 = 'That is a really bad movie '
text5 = 'This is a terrible movie'

# 构建TextCollection对象
tc = TextCollection([text1, text2, text3, 
                        text4, text5])
new_text = 'That one is a good movie. This is so good!'
word = 'That'
tf_idf_val = tc.tf_idf(word, new_text)
print('{}的TF-IDF值为：{}'.format(word, tf_idf_val))

That的TF-IDF值为：0.02181644599700369

微博情感分析：

步骤：

1. 文本读取
2. 分割训练集、测试集
3. 特征提取
4. 模型训练、预测

tools.py

# -*- coding: utf-8 -*-

import re
import jieba.posseg as pseg
import pandas as pd
import math
import numpy as np

# 加载常用停用词
stopwords1 = [line.rstrip() for line in open('./中文停用词库.txt', 'r', encoding='utf-8')]
# stopwords2 = [line.rstrip() for line in open('./哈工大停用词表.txt', 'r', encoding='utf-8')]
# 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')]
# stopwords = stopwords1 + stopwords2 + stopwords3
stopwords = stopwords1


def proc_text(raw_line):
    """
        处理每行的文本数据
        返回分词结果
    """
    # 1. 使用正则表达式去除非中文字符
    filter_pattern = re.compile('[^\u4E00-\u9FD5]+')
    chinese_only = filter_pattern.sub('', raw_line)

    # 2. 结巴分词+词性标注
    words_lst = pseg.cut(chinese_only)

    # 3. 去除停用词
    meaninful_words = []
    for word, flag in words_lst:
        # if (word not in stopwords) and (flag == 'v'):
            # 也可根据词性去除非动词等
        if word not in stopwords:
            meaninful_words.append(word)

    return ' '.join(meaninful_words)


def split_train_test(text_df, size=0.8):
    """
        分割训练集和测试集
    """
    # 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
    train_text_df = pd.DataFrame()
    test_text_df = pd.DataFrame()

    labels = [0, 1, 2, 3]
    for label in labels:
        # 找出label的记录
        text_df_w_label = text_df[text_df['label'] == label]
        # 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
        text_df_w_label = text_df_w_label.reset_index()

        # 默认按80%训练集，20%测试集分割
        # 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
        # 当然也可以随机拆分80%，20%（尝试实现下DataFrame中的随机拆分）

        # 该类数据的行数
        n_lines = text_df_w_label.shape[0]
        split_line_no = math.floor(n_lines * size)
        text_df_w_label_train = text_df_w_label.iloc[:split_line_no, :]
        text_df_w_label_test = text_df_w_label.iloc[split_line_no:, :]

        # 放入整体训练集，测试集中
        train_text_df = train_text_df.append(text_df_w_label_train)
        test_text_df = test_text_df.append(text_df_w_label_test)

    train_text_df = train_text_df.reset_index()
    test_text_df = test_text_df.reset_index()
    return train_text_df, test_text_df


def get_word_list_from_data(text_df):
    """
        将数据集中的单词放入到一个列表中
    """
    word_list = []
    for _, r_data in text_df.iterrows():
        word_list += r_data['text'].split(' ')
    return word_list


def extract_feat_from_data(text_df, text_collection, common_words_freqs):
    """
        特征提取
    """
    # 这里只选择TF-IDF特征作为例子
    # 可考虑使用词频或其他文本特征作为额外的特征

    n_sample = text_df.shape[0]
    n_feat = len(common_words_freqs)
    common_words = [word for word, _ in common_words_freqs]

    # 初始化
    X = np.zeros([n_sample, n_feat])
    y = np.zeros(n_sample)

    print('提取特征...')
    for i, r_data in text_df.iterrows():
        if (i + 1) % 5000 == 0:
            print('已完成{}个样本的特征提取'.format(i + 1))

        text = r_data['text']

        feat_vec = []
        for word in common_words:
            if word in text:
                # 如果在高频词中，计算TF-IDF值
                tf_idf_val = text_collection.tf_idf(word, text)
            else:
                tf_idf_val = 0

            feat_vec.append(tf_idf_val)

        # 赋值
        X[i, :] = np.array(feat_vec)
        y[i] = int(r_data['label'])

    return X, y


def cal_acc(true_labels, pred_labels):
    """
        计算准确率
    """
    n_total = len(true_labels)
    correct_list = [true_labels[i] == pred_labels[i] for i in range(n_total)]

    acc = sum(correct_list) / n_total
    return acc

main.py

# -*- coding: utf-8 -*-

import os
import pandas as pd
import nltk
from tools import proc_text, split_train_test, get_word_list_from_data, \
    extract_feat_from_data, cal_acc
from nltk.text import TextCollection
from sklearn.naive_bayes import GaussianNB

dataset_path = './dataset'
text_filenames = ['0_simplifyweibo.txt', '1_simplifyweibo.txt',
                  '2_simplifyweibo.txt', '3_simplifyweibo.txt']

# 原始数据的csv文件
output_text_filename = 'raw_weibo_text.csv'

# 清洗好的文本数据文件
output_cln_text_filename = 'clean_weibo_text.csv'

# 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
# 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
# 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
is_first_run = True


def read_and_save_to_csv():
    """
        读取原始文本数据，将标签和文本数据保存成csv
    """

    text_w_label_df_lst = []
    for text_filename in text_filenames:
        text_file = os.path.join(dataset_path, text_filename)

        # 获取标签，即0, 1, 2, 3
        label = int(text_filename[0])

        # 读取文本文件
        with open(text_file, 'r', encoding='utf-8') as f:
            lines = f.read().splitlines()

        labels = [label] * len(lines)

        text_series = pd.Series(lines)
        label_series = pd.Series(labels)

        # 构造dataframe
        text_w_label_df = pd.concat([label_series, text_series], axis=1)
        text_w_label_df_lst.append(text_w_label_df)

    result_df = pd.concat(text_w_label_df_lst, axis=0)

    # 保存成csv文件
    result_df.columns = ['label', 'text']
    result_df.to_csv(os.path.join(dataset_path, output_text_filename),
                     index=None, encoding='utf-8')


def run_main():
    """
        主函数
    """
    # 1. 数据读取，处理，清洗，准备
    if is_first_run:
        print('处理清洗文本数据中...', end=' ')
        # 如果是第一次运行需要对原始文本数据进行处理和清洗

        # 读取原始文本数据，将标签和文本数据保存成csv
        read_and_save_to_csv()

        # 读取处理好的csv文件，构造数据集
        text_df = pd.read_csv(os.path.join(dataset_path, output_text_filename),
                              encoding='utf-8')

        # 处理文本数据
        text_df['text'] = text_df['text'].apply(proc_text)

        # 过滤空字符串
        text_df = text_df[text_df['text'] != '']

        # 保存处理好的文本数据
        text_df.to_csv(os.path.join(dataset_path, output_cln_text_filename),
                       index=None, encoding='utf-8')
        print('完成，并保存结果。')

    # 2. 分割训练集、测试集
    print('加载处理好的文本数据')
    clean_text_df = pd.read_csv(os.path.join(dataset_path, output_cln_text_filename),
                                encoding='utf-8')
    # 分割训练集和测试集
    train_text_df, test_text_df = split_train_test(clean_text_df)
    # 查看训练集测试集基本信息
    print('训练集中各类的数据个数：', train_text_df.groupby('label').size())
    print('测试集中各类的数据个数：', test_text_df.groupby('label').size())

    # 3. 特征提取
    # 计算词频
    n_common_words = 200

    # 将训练集中的单词拿出来统计词频
    print('统计词频...')
    all_words_in_train = get_word_list_from_data(train_text_df)
    fdisk = nltk.FreqDist(all_words_in_train)
    common_words_freqs = fdisk.most_common(n_common_words)
    print('出现最多的{}个词是：'.format(n_common_words))
    for word, count in common_words_freqs:
        print('{}: {}次'.format(word, count))
    print()

    # 在训练集上提取特征
    text_collection = TextCollection(train_text_df['text'].values.tolist())
    print('训练样本提取特征...', end=' ')
    train_X, train_y = extract_feat_from_data(train_text_df, text_collection, common_words_freqs)
    print('完成')
    print()

    print('测试样本提取特征...', end=' ')
    test_X, test_y = extract_feat_from_data(test_text_df, text_collection, common_words_freqs)
    print('完成')

    # 4. 训练模型Naive Bayes
    print('训练模型...', end=' ')
    gnb = GaussianNB()
    gnb.fit(train_X, train_y)
    print('完成')
    print()

    # 5. 预测
    print('测试模型...', end=' ')
    test_pred = gnb.predict(test_X)
    print('完成')

    # 输出准确率
    print('准确率：', cal_acc(test_y, test_pred))

if __name__ == '__main__':
    run_main()