Weibo (a Microblog site popular in China) User Sentimental Influence Analysis Results

1. Collab Image
2. User Ranklist

• Analysis text contents of Weibo and extract sentimental infomation with Python.
• Define the weight of the followed and following link relationship with Python.
• Classify and rank sentimental influence of Weibo users among the SNS using PageRank iteration process.
• Visulize sentimental analysis results of ranking and relationship network of users with Matplotlib and D3.js.

Use Word2Vec to do the Word Embedding to represent words. Use Recurrent Neural Network(RNN)/LSTM (tutorial: keras) to train the sentimental classification.

1. get data and input data into matrix
2. Use jieba to cut the Chinese sentence into words
3. Word2Vec model set up, training, testing, fine-tune, training, testing, fine-tune...... until -> :)
4. Get the three types of sentiments: positive, negative and neutral of these sentences.
   

Final target is: Second-level domain: https://jeness.github.io/WeiboRankEngVer/CollabEngVer/ -> weiborankengver.haoranyu.info/CollabEngVer main page domain: 'jeness.github.io' -> www.haoranyu.info

Step 1: In gh-pages branch in WeiboRankEngVer repo, add CNAME file, add weiborankengver.haoranyu.info in the CNAME file.

Step 2: add a new CNAME in godday

http://zangbo.me/2017/06/12/GithubPageDomainName/