SnowNLP: Python Library for Chinese NLP

1 year ago
Olivia Parker
2 minutes

snownlp is a Python-based natural language processing (NLP) library used for processing Chinese text. It offers a range of features such as text classification, sentiment analysis, word segmentation, and keyword extraction.

Here are some common uses of SnowNLP:

  1. Word segmentation: Snownlp can be used to segment Chinese texts, breaking sentences into individual words. For example:
from snownlp import SnowNLP

text = "我喜欢自然语言处理"
s = SnowNLP(text)
words = s.words
print(words)

The output result is: [‘I’, ‘like’, ‘natural language’, ‘processing’]

  1. Sentiment analysis: Snownlp can analyze the sentiment of Chinese text and determine the emotional tendency of the text. For example:
from snownlp import SnowNLP

text = "这部电影太好看了"
s = SnowNLP(text)
sentiment = s.sentiments
print(sentiment)

The output is 0.9978232200000001 (closer to 1 indicates positive emotion).

  1. Key phrase extraction: Snownlp can be used to extract key phrases from text. For example:
from snownlp import SnowNLP

text = "这本书非常有趣，关于自然语言处理的内容很丰富"
s = SnowNLP(text)
keywords = s.keywords(limit=5)
print(keywords)

The output is: [‘Natural language’, ‘Fun’, ‘Content’, ‘Rich’, ‘Books’]

  1. Text categorization: Snownlp can classify text into different categories. For example:
from snownlp import SnowNLP
from snownlp import seg

sentences = [("这部电影非常精彩", "积极"), ("这个产品质量很差", "消极"), ("这个新闻报道很客观", "中立")]

def get_features(text):
    words = seg.seg(text)
    return dict([(word, True) for word in words])

train_data = [(get_features(text), label) for text, label in sentences]
classifier = SnowNLP.train(train_data)
text = "这是一篇很好的报道"
features = get_features(text)
result = classifier.classify(features)
print(result)

The output result is “neutral”

These are just some common uses of snownlp, it also has many other features and methods for processing and analyzing Chinese text.

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