Coursera课程自然语言处理(NLP)笔记整理(一)

1. 有监督学习(Supervised Machine Learning)

在有监督学习中，有输入特征X和标签集合Y，目标是最小化错误率或cost。
为了达成这个目标，需要运行预测函数（用参数数据将你的特征X映射到标签Y上），损失函数会比较得到的outputY与标签Y的距离，然后更新你的参数，重复整个过程，直到cost最小。

1.1. 情感分析(Sentiment Analysis)

目标： 预测一个tweeta是积极还是消极的情感
训练集： 积极情感的label为1，消极情感的label为0

方式： 逻辑回归分类器(logistic regression model)

1.2. 初步处理

1.2.1. 建立Vocabulary

将text转变为vector
例：

text = "I am happy because I am learning NLP"
Vocabulary = [I,am,happy,because,learning,NLP]
#不重复添加 I、am

1.2.2. 提取特征

对喜欢语句中出现的词语赋予1
如

[I,am,happy,because,learning,NLP,...,hated,the,movie]
[1,1,1,1,1,1,...,0,0,0]

这种仅有少数值为非零值的表示称之为稀疏表示。
使用逻辑回归模型将要学习n+1个参数，其中n为Vocabulary的大小，这是十分浪费的。 为什么多一个？

1.3. 分类与频率统计

语料集Corpus

I am happy because I am learning NLP
I am happy
I am sad, I am not learning NLP
I am sad

从语料集中可以建立Vocabulary
Vocabulary

I
am
happy
because
learning
NLP
sad
not

分类
语料集的tweets可以分为两类
Positive tweets

I am happy because I am learning NLP
I am happy

Negative tweets

I am sad, I am not learning NLP
I am sad

统计Vocabulary中的词语在Positive tweets中出现的频率

Vocabulary	PosFreq(1)
I	3
am	3
happy	2
because	1
learning	1
NLP	1
sad	0
not	0

同理课统计在Negative tweets中出现的频率
有

Vocabulary	PosFreq(1)	NegFreq(0)
I	3	3
am	3	3
happy	2	0
because	1	0
learning	1	1
NLP	1	1
sad	0	2
not	0	1

在实际代码中，这个表可以通过字典映射来实现。

现在只需要学习三个特征（？？？）

1.4. 频率特征

arbitrary tweet m

$$X_m=[1,{\Sigma }_{\omega }freqs(\omega ,1),{\Sigma }_{\omega }freqs(\omega ,0)]$$

• 第一项为偏差单位，为1
• 第二项为每个单词的积极频率和
• 第三项为每个单词的消极频率和

例：
I am sad, I am not learning NLP
Vocabulary中未出现的单词为happy和because
所以可以求出

$$X_m=[1,8,11]$$

1.5. 预处理

要点

• 词干提取(stemming)
• 停顿词(stop words)

例：

@YM and @Andrew are tuning a GREAT AI model at https://deeplearning.ai!!!

1. 去除不重要的单词，停顿词和标点符号。比如可以去掉存在于下表中的字符。

Stop words	Punctuation
and	,
is	.
are	:
at	!
has	"
for	’
a

则原句变为

@YM @Andrew tuning GREAT AI model https://deeplearning.ai!!!

（PS：在某些内容中你不需要去除标点符号，他们可能对句意有重要意义）

2. 去除URL和Handles

tuning GREAT AI model

3. 词干提取
tune/ed/inging GREAT AI model
通过这种操作可以大幅减小Vocabulary

2. 初步实验

使用package Natural Language Toolkit(NLTK)

pip install NLTK

这是一个开源的自然语言处理库，包括collecting、handling、processing的模块
使用NLTK中的twitter dataset来进行练习，其已经被手工标注。

2.1. 导入工具包

import nltk                                # Python library for NLP
from nltk.corpus import twitter_samples    # sample Twitter dataset from NLTK
import matplotlib.pyplot as plt            # library for visualization
import random                              # pseudo-random number generator

2.2. 关于数据集

NLTK中的这个样本数据集被分成了5000个积极和和5000个消极的tweets，两边数量恰好相等是为了有一个平衡的数据集。这并不意味着真实的积极、消极分布比例。
这只是因为平衡的数据集能够简化情感分析中绝大多数的计算方法。

• 数据集下载
  在私人电脑上，需要下载数据集

# downloads sample twitter dataset. uncomment the line below if running on a local machine.
nltk.download('twitter_samples')

• tweets加载
  使用模块的strings来进行加载

# select the set of positive and negative tweets
all_positive_tweets = twitter_samples.strings('positive_tweets.json')
all_negative_tweets = twitter_samples.strings('negative_tweets.json')

• 打印数据集信息

print('Number of positive tweets: ', len(all_positive_tweets))
print('Number of negative tweets: ', len(all_negative_tweets))

print('\nThe type of all_positive_tweets is: ', type(all_positive_tweets))
print('The type of a tweet entry is: ', type(all_negative_tweets[0]))

可以获得结果

    Number of positive tweets:  5000
    Number of negative tweets:  5000

    The type of all_positive_tweets is:  
    The type of a tweet entry is:  

可以使用Matplotlib的pyplot库来获得更多可视化信息，如饼图

# Declare a figure with a custom size
fig = plt.figure(figsize=(5, 5))

# labels for the two classes
labels = 'Positives', 'Negative'

# Sizes for each slide
sizes = [len(all_positive_tweets), len(all_negative_tweets)] 

# Declare pie chart, where the slices will be ordered and plotted counter-clockwise:
plt.pie(sizes, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=90)

# Equal aspect ratio ensures that pie is drawn as a circle.
plt.axis('equal')  

# Display the chart
plt.show()

2.3. 数据处理

在数据科学项目中，理解数据对80%的成功或失败负有责任。我们可以在预处理数据时对各种问题进行考虑。

• 打印案例
  随机打印一条正推和一条负推。我们在字符串的开头添加了一个颜色标记，以进一步区分两者。

# print positive in greeen
print('\033[92m' + all_positive_tweets[random.randint(0,5000)])

# print negative in red
print('\033[91m' + all_negative_tweets[random.randint(0,5000)])

很多推文中都有表情符号和url。这个信息将在接下来的步骤中派上用场

2.3.1. 对原始文本进行预处理

预处理包括以下任务：

1. tokenizing the string 标记字符串
2. Lowercasing 小写
3. Removing stop words and punctuation 去除停顿词和标点
4. Stemming 词干提取

先选择一个来完成这个步骤

# Our selected sample. Complex enough to exemplify each step
tweet = all_positive_tweets[2277]
print(tweet)

得到

My beautiful sunflowers on a sunny Friday morning off :) #sunflowers #favourites #happy #Friday off… https://t.co/3tfYom0N1i

下载并加载一些必要的库

# download the stopwords from NLTK
nltk.download('stopwords')

import re                                  # library for regular expression operations
import string                              # for string operations

from nltk.corpus import stopwords          # module for stop words that come with NLTK
from nltk.stem import PorterStemmer        # module for stemming
from nltk.tokenize import TweetTokenizer   # module for tokenizing strings

2.3.1.1. 删除超链接、Twitter标记和样式

使用re库对tweet执行正则表达式操作
正则表达式的相关知识可以参考

• 菜鸟教程-正则表达式
• 菜鸟教程-python中的正则表达式

print('\033[92m' + tweet)
print('\033[94m')

# remove old style retweet text "RT"
tweet2 = re.sub(r'^RT[\s]+', '', tweet)

# remove hyperlinks
tweet2 = re.sub(r'https?:\/\/.*[\r\n]*', '', tweet2)

# remove hashtags
# only removing the hash # sign from the word
tweet2 = re.sub(r'#', '', tweet2)

print(tweet2)

其输出结果有

My beautiful sunflowers on a sunny Friday morning off :) #sunflowers #favourites #happy #Friday off… https://t.co/3tfYom0N1i

My beautiful sunflowers on a sunny Friday morning off :) sunflowers favourites happy Friday off… 

去除了不必要的字符

2.3.2. 标记字符串 Tokenize the string

将字符串分割(token)为单个单词，不带空格或制表符。我们还将字符串中的每个单词转换为小写。NLTK的tokenize模块可以轻松地完成这些任务。

print()
print('\033[92m' + tweet2)
print('\033[94m')

# instantiate tokenizer class
tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,
                               reduce_len=True)

# tokenize tweets
tweet_tokens = tokenizer.tokenize(tweet2)

print()
print('Tokenized string:')
print(tweet_tokens)

可得结果

My beautiful sunflowers on a sunny Friday morning off :) sunflowers favourites happy Friday off… 


Tokenized string:
['my', 'beautiful', 'sunflowers', 'on', 'a', 'sunny', 'friday', 'morning', 'off', ':)', 'sunflowers', 'favourites', 'happy', 'friday', 'off', '…']

2.3.3. 去除停顿词和标点

查看一些库里的stop words

#Import the english stop words list from NLTK
stopwords_english = stopwords.words('english') 

print('Stop words\n')
print(stopwords_english)

print('\nPunctuation\n')
print(string.punctuation)

有结果

Stop words

['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]

Punctuation

!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~

在实际应用中，我们可能仍然需要对这两个列表进行定制，如是tweets中:)和…常常含有语意

接下来清除tweet里的这些词语

print()
print('\033[92m')
print(tweet_tokens)
print('\033[94m')

tweets_clean = []

for word in tweet_tokens: # Go through every word in your tokens list
    if (word not in stopwords_english and  # remove stopwords
        word not in string.punctuation):  # remove punctuation
        tweets_clean.append(word)

print('removed stop words and punctuation:')
print(tweets_clean)

得到

['my', 'beautiful', 'sunflowers', 'on', 'a', 'sunny', 'friday', 'morning', 'off', ':)', 'sunflowers', 'favourites', 'happy', 'friday', 'off', '…']

removed stop words and punctuation:
['beautiful', 'sunflowers', 'sunny', 'friday', 'morning', ':)', 'sunflowers', 'favourites', 'happy', 'friday', '…']

2.3.4. 词干分析 Stemming

词干提取是将一个单词转换为其最一般的形式或词干的过程。这有助于减少我们的词汇量。
比如

• learn
• learning
• learned
• learnt
  都来源于相同的词根learn
  但是，在某些情况下，词干分析过程生成的单词不是根单词的正确拼写。例如，happi和sunni。
• happy
• happiness
• happier
  在以上三个词中，happi是共有最多的，而如果选用happ又会和happen搞混
  接下来
  使用PorterStemmer模块，该模块使用Porter词干提取算法。

print()
print('\033[92m')
print(tweets_clean)
print('\033[94m')

# Instantiate stemming class
stemmer = PorterStemmer() 

# Create an empty list to store the stems
tweets_stem = [] 

for word in tweets_clean:
    stem_word = stemmer.stem(word)  # stemming word
    tweets_stem.append(stem_word)  # append to the list

print('stemmed words:')
print(tweets_stem)

获得结果

['beautiful', 'sunflowers', 'sunny', 'friday', 'morning', ':)', 'sunflowers', 'favourites', 'happy', 'friday', '…']

stemmed words:
['beauti', 'sunflow', 'sunni', 'friday', 'morn', ':)', 'sunflow', 'favourit', 'happi', 'friday', '…']

现在我们基本上就完成了words的预处理

3. 作业

使用utils.py里的函数process_tweet(tweet)来实习今天的工作。
如

from utils import process_tweet # Import the process_tweet function

# choose the same tweet
tweet = all_positive_tweets[2277]

print()
print('\033[92m')
print(tweet)
print('\033[94m')

# call the imported function
tweets_stem = process_tweet(tweet); # Preprocess a given tweet

print('preprocessed tweet:')
print(tweets_stem) # Print the result

得到结果

My beautiful sunflowers on a sunny Friday morning off :) #sunflowers #favourites #happy #Friday off… https://t.co/3tfYom0N1i

preprocessed tweet:
['beauti', 'sunflow', 'sunni', 'friday', 'morn', ':)', 'sunflow', 'favourit', 'happi', 'friday', '…']