python
import jieba
seg_list = jieba.cut(text)
print(list(seg_list))
python
from pydeep.tools import stopwords
cleaned_text = stopwords.remove_stopwords(text)
print(cleaned_text)
python
from pydeep.models.word2vec import Word2Vec
model = Word2Vec(text, size=100, window=5, min_count=1)
vectors = model.get_vectors()
print(vectors)
python
from pydeep.metrics import cosine_similarity
vector1 = [0.2, 0.4, 0.6]
vector2 = [0.1, 0.7, 0.4]
similarity = cosine_similarity(vector1, vector2)
print(similarity)
python
from pydeep.sentiment import SentimentClassifier
classifier = SentimentClassifier()
sentiment = classifier.predict(text)
print(sentiment)
