讲解：Stock Market、Python、FINAL PROJECT、PythonMatlab|P

FINAL PROJECT: Analysis of President Trump’s Tweets and Stock Market performance.The project will involve textual pre-processing, data cleaning, word embedding, and running regressionsin Python. There are two parts to it.Part I requires you to clean twitter data and look for relevant words. You need to find tweets containingthe word stock (for stock market) and a second a term of your own choosing. For instance, you can lookfor tweets talking about china, war, trade, unfair, great, border, refugee (anything really). Then you willmerge twitter data with stock market indices. Here also you will run some word embedding and seewhat word2vec model can learn about meaning of words through Trump’s tweets.In Part II you will run regressions on stock market indices. You need to run regressions for:One of the dummy variables has to be a dummy for stock tweets and the second one of your choice (i.e.dummy for tweets mentioning china, war, trade and so on). Also for your second term, you need runadditional regressions with sentiment analysis dummies (positive and negative). You will run theseregressions for 2 time periods (2010- now and 2016-now). There are 12 indices, so in total you will have96 regressions.There are additional details in Python script/file itself on what to do and what’s required.For this project, I would highly recommend代写Stock Market作业、代做Python程序设计作业、代写FINAL PROJECT作业、代做Python编程 everyone to use Jupyter Notebook. Jupyter Notebook isanother Python code reader, but it allows you to run one line at a time, and it will be extremely usefulfor this project.Instructions to Install Jupyter Notebook for Mac Users:1. Pip3 install jupyter notebookWindows Users:1. Python -m pip install jupyter notebookHere is a tutorial/article on how to use it from CodeAcademy.The due date for this project is midnight of December 7th. You will need to submit the following threethings:1. Python code2. (HTML) Output of regressions3. Word File:a. Interpretations of regression outputb. Limitations of these regressionsYou will need to download Part I and Part II Python files (available both in Jupyter Notebook and plainPython format) and stock market data csv. For this assignment you can work in groups. At most there can be 3 people in a group, and you can workon the same code and write-up. However, for each member of the group, there should be acorresponding number of extra terms (i.e. tweets containing the word “stock” + extra terms, such astrade, war, border, refugee). For extra terms, do not forget to do the sentiment analysis and run all theregressions. When submitting the assignment as a group, submit the same version of the code andwrite-up separately so it is easier to track who submitted what. 转自：http://ass.3daixie.com/2018120813266652.html