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| − | ==Social Media Sentiment Analysis==
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| − | ===Motivation===
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| − | Social media has almost become synonymous with «big data» due to the sheer amount of user-generated content.
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| − | Mining this rich data can prove unprecedented ways to keep a pulse on opinions, trends, and public sentiment. Facebook, Twitter, YouTube, WeChat... etc.
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| − | Social media data will become even more relevant for marketing, branding, and business as a whole.
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| − | As you see, this kind of analysis is a tool that will become more and more important in the coming years.
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| − | ===Methodology===
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| − | * The first part of the project will be: '''Mining Social Media data'''
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| − | ** To start the project we first need to choose where we are going to get the data from. I have seen in many sources that to start working on it, Twitter is the classic entry point for practicing. Here you can see a tutorial about how to '''mining Twitter Data with Python''' : https://marcobonzanini.com/2015/03/02/mining-twitter-data-with-python-part-1/
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| − | * Secondly, we will need to store the data.
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| − | * The third part of the project will be the analysis of the data. Here is where Machine learning will be implement.
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| − | ** In this part we first need to decide what we want to analysis. There are many examples, here is a nice work I found: «This article describes the techniques that effectively analyzed Twitter Trend Topics to predict, as a sample test case, regional voting patterns in the 2014 Brazilian presidential election» : https://www.toptal.com/data-science/social-network-data-mining-for-predictive-analysis
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| − | :: In essence, this guy analyses Twitter data for the days prior to the election and got this mapa:
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| − | [[File:Brazilian_elections_2014.png|950px|thumb|center|]]
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| − | https://www.dezyre.com/article/top-10-machine-learning-projects-for-beginners/397
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| − | https://elitedatascience.com/machine-learning-projects-for-beginners#social-media
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| − | https://en.wikipedia.org/wiki/Sentiment_analysis
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| − | https://en.wikipedia.org/wiki/Social_media_mining
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| − | ==Remote development==
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| − | ===Eclipse - Connect to a remote file system===
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| − | https://us.informatiweb.net/tutorials/it/6-web/148--eclipse-connect-to-a-remote-file-system.html
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| − | ===Mount a remote filesystem in your local machine===
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| − | https://www.digitalocean.com/community/tutorials/how-to-use-sshfs-to-mount-remote-file-systems-over-ssh
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| − | https://stackoverflow.com/questions/32747819/remote-java-development-using-intellij-or-eclipse
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| − | https://serverfault.com/questions/306796/sshfs-problem-when-losing-connection
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| − | https://askubuntu.com/questions/358906/sshfs-messes-up-everything-if-i-lose-connection
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| − | https://askubuntu.com/questions/716612/sshfs-auto-reconnect
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| − | root@sinfronteras.ws: /home/adelo/1-system/3-cloud
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| − | sshfs -o reconnect,ServerAliveInterval=5,ServerAliveCountMax=3 root@sinfronteras.ws: /home/adelo/1-system/3-cloud
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| − | sshfs -o allow_other root@sinfronteras.ws: /home/adelo/1-system/3-cloud
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| − | '''faster way to mount a remote file system than sshfs:'''
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| − | https://superuser.com/questions/344255/faster-way-to-mount-a-remote-file-system-than-sshfs
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| − | ==Anaconda==
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| − | Anaconda is a free and open source distribution of the Python and R programming languages for data science and machine learning related applications (large-scale data processing, predictive analytics, scientific computing), that aims to simplify package management and deployment. Package versions are managed by the package management system conda. https://en.wikipedia.org/wiki/Anaconda_(Python_distribution)
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| − | ===Installation===
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| − | https://www.anaconda.com/download/#linux
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| − | https://linuxize.com/post/how-to-install-anaconda-on-ubuntu-18-04/
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| − | https://www.digitalocean.com/community/tutorials/how-to-install-the-anaconda-python-distribution-on-ubuntu-18-04
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| − | ===Jupyter Notebook===
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| − | https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook
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| − | ==Cursos==
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| − | ===eu.udacity.com===
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| − | https://classroom.udacity.com/courses/ud120
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| − | ===www.coursera.org===
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| − | https://www.coursera.org/learn/machine-learning/home/welcome
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| − | ===Otros===
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| − | https://www.udemy.com/machine-learning-course-with-python/
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| − | https://stackoverflow.com/questions/19181999/how-to-create-a-keyboard-shortcut-for-sublimerepl
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