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[数据+代码] 基于机器学习方法的中文情感分析,包括朴素贝叶斯、K近邻、支持向量机、Logistic、随机森林、决策树、梯度增强决策树等模型的训练和测试...

Home Page: https://github.com/DavionWu2018/Sentiment_machine_learning

Jupyter Notebook 83.89% Python 16.11%
jupyter machine-learning python3 sentiment-analysis

sentiment_machine_learning's Introduction

Project description:

[数据+代码] 基于机器学习方法的中文情感分析,包括朴素贝叶斯、K近邻、支持向量机、Logistic、随机森林、决策树、梯度增强决策树等模型的训练和测试... 分析流程主要包括:数据预处理-分词-特征提取-训练-测试-分析数据

How to use:

1)打开Code_Davion文件夹下的python程序,在jupyter中运行,注意对应文件的存放位置;
2)查看 data.xlsx 、 data_result.xlsx 和 data_test_train.xlsx 示例文件,将对应数据替换成待分析文本即可;
3)data_test_train.xlsx 为带标签的文本,需要手工标注,用作模型训练和测试;
4)data.xlsx 为待分析的文本数据,应当与标签文本的数据不重合(示例数据请自行替换);

Contact me:

👋 Hi, I’m @DavionWu2018
👀 I’m interested in sustainable tourism, tourism firm management, text mining, and event study.
🌱 I’m currently learning tourism management.
💞️ I’m looking to collaborate on text mining of tourism big data.
📫 How to reach me: [email protected].

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