基于SNOWNLP和最大熵的中文情感二分类代码
时间: 2024-03-24 10:37:16 浏览: 24
以下是基于SNOWNLP和最大熵的中文情感二分类代码的示例:
```python
import jieba
from snownlp import SnowNLP
from collections import defaultdict
import codecs
import os
import sys
import pickle
import math
# 加载停用词
def load_stopwords():
stopwords = set()
with codecs.open('stopwords.txt', 'r', encoding='utf-8') as f:
for line in f:
stopwords.add(line.strip())
return stopwords
# 加载语料
def load_corpus():
corpus = []
with codecs.open('corpus.txt', 'r', encoding='utf-8') as f:
for line in f:
corpus.append(line.strip())
return corpus
# 分词
def segment(text, stopwords):
words = []
for word in jieba.cut(text):
if word not in stopwords:
words.append(word)
return words
# 计算词频
def count_words(words):
word_count = defaultdict(int)
for word in words:
word_count[word] += 1
return word_count
# 特征提取
def extract_features(text, stopwords):
features = {}
words = segment(text, stopwords)
word_count = count_words(words)
for word, count in word_count.items():
features[word] = count
return features
# 训练模型
def train_model():
# 加载停用词和语料
stopwords = load_stopwords()
corpus = load_corpus()
# 特征提取
featuresets = []
for text in corpus:
features = extract_features(text, stopwords)
label = SnowNLP(text).sentiments
featuresets.append((features, label))
# 划分训练集和测试集
train_size = int(len(featuresets) * 0.8)
train_set = featuresets[:train_size]
test_set = featuresets[train_size:]
# 训练分类器
classifier = nltk.classify.MaxentClassifier.train(train_set, algorithm='GIS', max_iter=10)
# 保存模型
with open('sentiment_classifier.pkl', 'wb') as f:
pickle.dump(classifier, f)
# 测试模型
accuracy = nltk.classify.accuracy(classifier, test_set)
print('Accuracy:', accuracy)
# 加载模型
def load_model():
with open('sentiment_classifier.pkl', 'rb') as f:
classifier = pickle.load(f)
return classifier
# 预测情感
def predict_sentiment(text, classifier):
stopwords = load_stopwords()
features = extract_features(text, stopwords)
label = classifier.classify(features)
return label
# 示例
if __name__ == '__main__':
# 训练模型
train_model()
# 加载模型
classifier = load_model()
# 预测情感
text = '这部电影真心不错'
label = predict_sentiment(text, classifier)
print(text, label)
text = '这个产品质量太差了'
label = predict_sentiment(text, classifier)
print(text, label)
```
说明:
1. 代码中使用了jieba分词库和snownlp情感分析库。
2. stopwords.txt是停用词文件,corpus.txt是语料文件,需要自己准备。
3. extract_features函数将文本转化为特征向量,使用了词频作为特征。
4. 训练模型使用了最大熵分类器。
5. predict_sentiment函数使用训练好的模型预测情感。
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