深入了解Python在自然语言处理中的创新应用

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist

# 加载英文停用词
nltk.download('stopwords')

# 加载文本数据
text = "The quick brown fox jumps over the lazy dog."

# 文本分词
tokens = word_tokenize(text.lower())

# 去除停用词
stop_words = set(stopwords.words('english'))
tokens = [word for word in tokens if word.isalpha() and word not in stop_words]

# 词频统计
freq_dist = FreqDist(tokens)

# 输出词频结果
for word, freq in freq_dist.items():
    print(word, freq)

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载情感分类数据集
# ...

# 文本特征提取
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(text_list)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2)

# 训练模型
model = SVC()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

from googletrans import Translator

# 创建翻译器对象
translator = Translator(service_urls=['translate.google.cn'])

# 设置源语言和目标语言
src_lang = 'en'
target_lang = 'zh-CN'

# 待翻译的文本
text = "Hello, how are you?"

# 机器翻译
result = translator.translate(text, src=src_lang, dest=target_lang)

# 输出翻译结果
print(result.text)