开源免费文本数据标注工具使用：paddlehub snownlp

第一个snownlp ：

# pip install snownlp -i https://mirror.baidu.com/pypi/simple
from snownlp import SnowNLP
text="大牛市来啦，发财啦"
nlp=SnowNLP(text)
print(nlp.sentiments)

第二个 paddlehub：

会有很多包搭配的问题：

#https://blog.csdn.net/u010751000/article/details/106747421?utm_medium=distribute.pc_relevant.none-task-blog-2~default~baidujs_baidulandingword~default-0-106747421-blog-120609993.pc_relevant_blogantidownloadv1&spm=1001.2101.3001.4242.1&utm_relevant_index=2
# from snownlp import SnowNLP
# text="大牛市来啦，发财啦"
# nlp=SnowNLP(text)
# print(nlp.sentiments)
# pip install paddlehub==1.8.0 -i https://mirror.baidu.com/pypi/simple
# pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
# pip install protobuf==3.20.1 -i https://mirror.baidu.com/pypi/simple
# pip install opencv-contrib-python -i https://mirror.baidu.com/pypi/simple
# python -m pip install -U pip -i https://mirror.baidu.com/pypi/simple
import paddlehub as hub
import paddlehub as hub
# 加载模型
senta = hub.Module(name="senta_lstm")
# 待分类文本
test_text = [
    "三百多块比亚迪一股分红一毛，四百多的宁王分二毛呵呵这就是成长性",
    "刘背离死叉",
    "牛市来了",
    "$上证指数(SH000001)$主力这几天紧张、小心的计划割韭菜！小散们马上清仓",
    "散户明摆着被洗了一把，还没察觉呢，早盘低开回踩一"
]
# 情感分类
results = senta.sentiment_classify(data={"text": test_text})
# 得到结果
for result in results:
    print(result)

具体的包的 配置：