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我来介绍如何使用TextBlob库实现简单的情感极性评分。
安装TextBlob
pip install textblob python -m textblob.download_corpora
基本用法示例
from textblob import TextBlob
# 1. 基础情感分析
text = "I love this product! It's amazing and wonderful."
blob = TextBlob(text)
# 情感极性：-1(负面) 到 1(正面)
print(f"极性: {blob.sentiment.polarity}")
print(f"主观性: {blob.sentiment.subjectivity}")
# 2. 分析多个文本
texts = [
"This is terrible, I hate it so much.",
"The weather is okay, not great but not bad.",
"Absolutely fantastic! Best experience ever!",
"It's a neutral statement without strong emotions.",
"This product broke after one day. Very disappointed."
]
for text in texts:
blob = TextBlob(text)
polarity = blob.sentiment.polarity
subjectivity = blob.sentiment.subjectivity
# 分类情感
if polarity > 0.2:
sentiment = "正面"
elif polarity < -0.2:
sentiment = "负面"
else:
sentiment = "中性"
print(f"文本: {text}")
print(f"情感: {sentiment} (极性: {polarity:.3f}, 主观性: {subjectivity:.3f})")
print("-" * 50)
实用函数封装
def analyze_sentiment(text):
"""
分析文本情感并返回详细结果
"""
blob = TextBlob(text)
polarity = blob.sentiment.polarity
subjectivity = blob.sentiment.subjectivity
# 情感分类
if polarity > 0.2:
sentiment_label = "正面"
emoji = "😊"
elif polarity < -0.2:
sentiment_label = "负面"
emoji = "😞"
else:
sentiment_label = "中性"
emoji = "😐"
# 主观性分类
if subjectivity > 0.5:
subjectivity_label = "主观"
else:
subjectivity_label = "客观"
return {
"text": text,
"polarity": polarity,
"subjectivity": subjectivity,
"sentiment": sentiment_label,
"emoji": emoji,
"subjectivity_type": subjectivity_label
}
def batch_sentiment_analysis(texts):
"""
批量分析多个文本的情感
"""
results = []
for text in texts:
result = analyze_sentiment(text)
results.append(result)
return results
def get_sentiment_score(text):
"""
获取标准化情感评分 (0-10)
"""
blob = TextBlob(text)
polarity = blob.sentiment.polarity
# 将 -1到1 映射到 0到10
score = (polarity + 1) * 5
return round(score, 1)
# 使用示例
if __name__ == "__main__":
# 单条文本分析
review = "The customer service was excellent and very helpful!"
result = analyze_sentiment(review)
print(f"文本: {result['text']}")
print(f"情感: {result['emoji']} {result['sentiment']}")
print(f"极性评分: {result['polarity']:.3f}")
print(f"主观性: {result['subjectivity']:.3f} ({result['subjectivity_type']})")
print(f"标准化评分 (0-10): {get_sentiment_score(review)}")
print("\n" + "="*50)
# 批量分析
reviews = [
"Excellent product! Highly recommended.",
"Not worth the money, very disappointed.",
"Average quality, nothing special.",
"Amazing! Better than expected.",
"Poor customer support and slow delivery."
]
results = batch_sentiment_analysis(reviews)
print("批量分析结果:")
print(f"{'文本':<50} {'情感':<8} {'极性':<8} {'评分':<8}")
print("-" * 80)
for r in results:
score = get_sentiment_score(r['text'])
print(f"{r['text'][:48]:<50} {r['emoji'] + ' ' + r['sentiment']:<8} {r['polarity']:<8.3f} {score:<8}")
高级功能示例
from textblob import TextBlob
import re
class SentimentAnalyzer:
"""情感分析器类"""
def __init__(self):
self.language = 'en'
def analyze_sentence_by_sentence(self, text):
"""逐句分析情感"""
blob = TextBlob(text)
sentences = blob.sentences
results = []
for sentence in sentences:
sentiment = sentence.sentiment
results.append({
'sentence': str(sentence),
'polarity': sentiment.polarity,
'subjectivity': sentiment.subjectivity
})
return results
def get_detailed_analysis(self, text):
"""详细分析文本"""
blob = TextBlob(text)
return {
'words': blob.words,
'word_count': len(blob.words),
'noun_phrases': blob.noun_phrases,
'sentences_count': len(blob.sentences),
'overall_sentiment': blob.sentiment
}
def sentiment_trend(self, text):
"""分析情感变化趋势"""
sentences = self.analyze_sentence_by_句子(text)
trend = []
for i, sentence in enumerate(sentences):
trend.append({
'position': i + 1,
'polarity': sentence['polarity']
})
# 计算趋势方向
if len(trend) > 1:
first_polarity = trend[0]['polarity']
last_polarity = trend[-1]['polarity']
direction = last_polarity - first_polarity
if direction > 0.3:
trend_direction = "积极趋势 📈"
elif direction < -0.3:
trend_direction = "消极趋势 📉"
else:
trend_direction = "平稳趋势 ➡"
else:
trend_direction = "单句，无趋势"
return {
'trend_points': trend,
'direction': trend_direction
}
# 使用高级功能
analyzer = SentimentAnalyzer()
# 逐句分析
long_text = """I was excited to try this product.
The packaging was nice but the quality was disappointing.
However, customer service solved my problems quickly."""
print("逐句分析:")
result = analyzer.analyze_sentence_by_句子(long_text)
for r in result:
print(f" 句子: {r['sentence']}")
print(f" 极性: {r['polarity']:.3f}")
print()
# 情感趋势
print("情感趋势:")
trend = analyzer.sentiment_trend(long_text)
print(f" 趋势方向: {trend['direction']}")
for point in trend['trend_points']:
print(f" 句子 {point['position']}: 极性 {point['polarity']:.3f}")
实际应用示例
# 产品评论分析
def analyze_product_reviews(reviews_file=None, reviews_list=None):
"""分析产品评论"""
if reviews_list:
reviews = reviews_list
else:
# 示例评论
reviews = [
"Best purchase ever! Works perfectly.",
"Good but could be better.",
"Total waste of money.",
"Decent product for the price.",
"I'm very satisfied with this purchase."
]
# 分析所有评论
total_polarity = 0
positive = negative = neutral = 0
for review in reviews:
blob = TextBlob(review)
polarity = blob.sentiment.polarity
total_polarity += polarity
if polarity > 0.2:
positive += 1
elif polarity < -0.2:
negative += 1
else:
neutral += 1
# 计算总体评分
avg_polarity = total_polarity / len(reviews)
print("=" * 40)
print("产品评论分析报告")
print("=" * 40)
print(f"总评论数: {len(reviews)}")
print(f"好评: {positive} ({positive/len(reviews)*100:.1f}%)")
print(f"中评: {neutral} ({neutral/len(reviews)*100:.1f}%)")
print(f"差评: {negative} ({negative/len(reviews)*100:.1f}%)")
print(f"平均情感极性: {avg_polarity:.3f}")
if avg_polarity > 0.2:
print("总体评价: 积极 👍")
elif avg_polarity < -0.2:
print("总体评价: 消极 👎")
else:
print("总体评价: 中性 👌")
# 运行分析
analyze_product_reviews()
TextBlob的优点是简单易用，适合快速实现情感分析功能，不过需要注意的是，它只支持英文，且准确率有限，对于复杂语境的理解可能不够准确，如果需要更好的中文支持或更高准确率,可以考虑使用更专业的NLP库。