用python代码实现中文文本分类, 先用jieba分词, 然后用gensim
时间: 2024-01-26 13:15:07 浏览: 28
好的,下面是用python代码实现中文文本分类的示例,先使用jieba分词,然后使用gensim进行主题建模和文本分类:
```python
import jieba
from gensim import corpora, models
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# 读取数据
def read_data(file_path):
data = []
labels = []
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
label, text = line.strip().split('\t')
data.append(text)
labels.append(label)
return data, labels
# 分词和预处理
def preprocess(data):
texts = []
for text in data:
# 分词
words = jieba.cut(text)
# 去除停用词和标点符号
words = [w for w in words if w not in stop_words and w != ' ' and w != '\n']
texts.append(words)
return texts
# 构建词典和向量表示
def build_corpus(texts):
# 构建词典
dictionary = corpora.Dictionary(texts)
# 构建向量表示
corpus = [dictionary.doc2bow(text) for text in texts]
return dictionary, corpus
# 训练LDA模型
def train_lda_model(corpus, num_topics=10, num_iterations=100):
lda_model = models.LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary, iterations=num_iterations)
return lda_model
# 将文本转换为主题权重向量
def get_topic_distribution(text, lda_model, dictionary):
# 将文本转换为向量表示
vec = dictionary.doc2bow(text)
# 获取主题权重向量
topic_distribution = lda_model[vec]
return topic_distribution
# 将文本转换为主题分布向量
def get_topic_vector(text, lda_model, dictionary, num_topics):
# 获取主题权重向量
topic_distribution = get_topic_distribution(text, lda_model, dictionary)
# 转换为主题分布向量
topic_vector = [0] * num_topics
for topic_id, weight in topic_distribution:
topic_vector[topic_id] = weight
return topic_vector
# 训练分类器
def train_classifier(X, y):
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练分类器
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# 在测试集上进行预测
y_pred = clf.predict(X_test)
# 输出分类报告
print(classification_report(y_test, y_pred))
return clf
# 加载停用词
stop_words = set()
with open('stop_words.txt', 'r', encoding='utf-8') as f:
for line in f:
stop_words.add(line.strip())
# 读取数据
data, labels = read_data('data.txt')
# 分词和预处理
texts = preprocess(data)
# 构建词典和向量表示
dictionary, corpus = build_corpus(texts)
# 训练LDA模型
lda_model = train_lda_model(corpus)
# 将文本转换为主题分布向量
X = [get_topic_vector(text, lda_model, dictionary, lda_model.num_topics) for text in texts]
# 训练分类器
clf = train_classifier(X, labels)
```
在这个示例中,我们先使用jieba对输入的文本进行分词和预处理,然后使用gensim构建词典和向量表示,并训练LDA模型。接着,我们将每个文本转换为主题分布向量,然后使用SVM进行分类,并输出分类报告。需要注意的是,在训练LDA模型时,我们使用了默认的参数,即主题数为10,迭代次数为100。在实际应用中,需要根据具体的情况对这些参数进行调整。