机器学习-初级进阶（自然语言处理 ）

一、自然语言处理

1. 要处理的问题

   对于多组对话中判断这段化是消极的还是积极的
   

2. 处理的短语数据

                                               Review  Liked
                            Wow... Loved this place.      1
                                  Crust is not good.      0
           Not tasty and the texture was just nasty.      0
   Stopped by during the late May bank holiday of...      1
   ...
   

3. 代码实现

   from sklearn.feature_extraction.text import CountVectorizer
   from sklearn.model_selection import train_test_split
   from sklearn.metrics import confusion_matrix
   from sklearn.naive_bayes import GaussianNB
   from nltk.stem.porter import PorterStemmer
   from nltk.corpus import stopwords
   import pandas as pd
   import re
   
   dataset = pd.read_csv("Restaurant_Reviews.tsv", delimiter='\t', quoting=3)
   corpus = []
   
   # 下载虚词词库
   # import nltk
   # nltk.download('stopwords')
   
   for i in range(1000):
       review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])  # 将非字母替换成空格
       review = review.lower()  # 将大写替换成小写
       review = review.split()
       ps = PorterStemmer()
       review = [ps.stem(word) for word in review if not word in set(stopwords.words("english"))]  # 清理虚词, ps.stem(word): 词根化，比如loved->love
       review = " ".join(review)
       corpus.append(review)
       # print(review)
   
   cv = CountVectorizer(max_features=1500)  # 取1500个出现次数最多的
   X = cv.fit_transform(corpus).toarray()  # 稀疏矩阵转换
   y = dataset.iloc[:, 1].values
   
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
   
   classifier = GaussianNB()
   classifier = classifier.fit(X_train, y_train)
   
   y_pred = classifier.predict(X_test)
   
   cm = confusion_matrix(y_test, y_pred)
   

   输出结果:
   混淆矩阵结果：

   array([[55, 42],
      [12, 91]], dtype=int64) 
   

   真确率为73% 不是很理想，还算可以