# 用于训练的评论 review_train = [' '.join(review_to_text(review)) for review in train_review_list] # 对于训练评论对应的恶意评论/正常评论 sentiment_train = train_sentiment_list # 用于测试的评论 review_test = [' '.join(review_to_text(review)) for review in test_review_list] # 对于测试评论对应的恶意评论/正常评论 sentiment_test = test_sentiment_list vectorizer = CountVectorizer(max_df=0.8, min_df=3) tfidftransformer = TfidfTransformer() # 先转换成词频矩阵，再计算TFIDF值 tfidf = tfidftransformer.fit_transform(vectorizer.fit_transform(review_train)) # 朴素贝叶斯中的多项式分类器 clf = MultinomialNB().fit(tfidf, sentiment_train)

这段代码看起来是用朴素贝叶斯模型来对评论进行分类，其中用到了词频矩阵和TF-IDF值来作为特征，同时使用了CountVectorizer和TfidfTransformer来进行预处理。训练集和测试集的评论都被转换成了词频矩阵和TF-IDF值，用MultinomialNB()来训练分类器，最后可以用该分类器对新的评论进行分类。

相关问题

翻译代码review_list, sentiment_list = load_corpus(file_path_pos,file_path_nag) # 将全部语料按1:4分为测试集与训练集 n = len(review_list) // 5 train_review_list, train_sentiment_list = review_list[n:], sentiment_list[n:] test_review_list, test_sentiment_list = review_list[:n], sentiment_list[:n] print('训练集数量： {}'.format(str(len(train_review_list)))) print('测试集数量： {}'.format(str(len(test_review_list)))) # 用于训练的评论 review_train = [' '.join(review_to_text(review)) for review in train_review_list] # 对于训练评论对应的正常评论/恶意评论 sentiment_train = train_sentiment_list # 用于测试的评论 review_test = [' '.join(review_to_text(review)) for review in test_review_list] # 对于测试评论对应的正常评论/恶意评论 sentiment_test = test_sentiment_list count_vec = CountVectorizer(max_df=0.8, min_df=3) tfidf_vec = TfidfTransformer()

这段代码的作用是加载语料库并将其分为训练集和测试集。首先，load_corpus函数从指定的文件路径加载评论和情感标签，并将它们分别存储在review_list和sentiment_list列表中。然后，该代码将全部语料按照1：4的比例分成训练集和测试集。接着，将训练集中的评论转换为字符串形式，并将其存储在review_train列表中；train_sentiment_list列表则包含与训练集中的评论对应的情感标签。同样，测试集中的评论和情感标签分别存储在review_test和sentiment_test列表中。最后，使用CountVectorizer和TfidfTransformer将训练集中的评论转换为矩阵表示。其中，CountVectorizer对文本进行词频统计，TfidfTransformer则将词频统计矩阵转换为TF-IDF权重矩阵。

报错ValueError: np.nan is an invalid document, expected byte or unicode string. 怎么修改import pandas as pd from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取电影评论数据集 data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') x = v.fit_transform(df['eview'].apply(lambda x: np.str_(x))) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) # 创建CountVectorizer对象进行词频统计和向量化 count_vectorizer = CountVectorizer() X_train_count = count_vectorizer.fit_transform(X_train) X_test_count = count_vectorizer.transform(X_test) # 创建TfidfVectorizer对象进行TF-IDF计算和向量化 tfidf_vectorizer = TfidfVectorizer() X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) # 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 classifier_count = LogisticRegression() classifier_count.fit(X_train_count, y_train) y_pred_count = classifier_count.predict(X_test_count) accuracy_count = accuracy_score(y_test, y_pred_count) print("Accuracy using CountVectorizer:", accuracy_count) # 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 classifier_tfidf = LogisticRegression() classifier_tfidf.fit(X_train_tfidf, y_train) y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) print("Accuracy using TfidfVectorizer:", accuracy_tfidf)

在代码中，变量名应该是 'data' 而不是 'df'，所以在使用 CountVectorizer 进行向量化的时候，应该使用 'data' 而不是 'df'，修改代码如下所示：

import pandas as pd 
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer 
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression 
from sklearn.metrics import accuracy_score 

# 读取电影评论数据集 
data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') 

# 分割数据集为训练集和测试集 
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) 

# 创建CountVectorizer对象进行词频统计和向量化 
count_vectorizer = CountVectorizer() 
X_train_count = count_vectorizer.fit_transform(X_train) 
X_test_count = count_vectorizer.transform(X_test) 

# 创建TfidfVectorizer对象进行TF-IDF计算和向量化 
tfidf_vectorizer = TfidfVectorizer() 
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) 
X_test_tfidf = tfidf_vectorizer.transform(X_test) 

# 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 
classifier_count = LogisticRegression() 
classifier_count.fit(X_train_count, y_train) 
y_pred_count = classifier_count.predict(X_test_count) 
accuracy_count = accuracy_score(y_test, y_pred_count) 
print("Accuracy using CountVectorizer:", accuracy_count) 

# 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 
classifier_tfidf = LogisticRegression() 
classifier_tfidf.fit(X_train_tfidf, y_train) 
y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) 
accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) 
print("Accuracy using TfidfVectorizer:", accuracy_tfidf)