/pbd_ufrpe

Vou de Que? - Algoritmo para analisar o sentimento referente a Uber, Cabify e 99pop

Primary LanguagePython

Projeto de Banco de Dados (2017.1) (Python + Tweepy API + Naive Bayes + MySQL)

Bacharelado em Sistemas de Informação - UFRPE

Twitter: @pbd_ufrpe

Index

1) How to create/execute

$ pip --version
  • 1.5.1) If you dont know how to change your python version at cmd look at: This question

  • 1.6) Install those python libs at cmd or terminal:

$ python -m pip install --upgrade pip
$ pip install tweepy

cp (python version) - cp34 or cp36
win - 32 or 64 bits
Its necessary to download a numpy at your python and windows version before continue Navigate to your numpy location example:

C:\Users\Aluno> cd downloads
C:\Users\Aluno\Downloads> pip install numpy-1.13.1+mkl-cp34-cp34m-win32.whl
$ pip install nltk
$ pip install scipy
$ pip install sklearn
$ pip install geopy
$ pip install matplotlib
  • 1.6) Run Script VouDeQue.sql at MySQL to create a new schema. If necessary change adress or password connection visit DataBase.py and choose equals at MySQL.

  • 1.7) Change your tokens at tweepyExample.py and run project to search tweets and classify with your own sentiment analysis. Choose 1- Positive, 2- Negative, 3- Neutral.

  • 1.8) All tweets found will be at doc.py, just run this archive to save at MySQL.

  • 1.9) In case of trouble try this tutorial: Miniconda tutorial

2) Objectives

  • Search tweets about Uber, Cabify or 99Pop using Python and Twitter API
  • Store tweets to MySQL
  • Sentiment Analysis using Naive Bayes
  • Make queries and graphs based on last searches at a database

3) Use Case Document

https://drive.google.com/open?id=0By7Vlsi01ABLSFFCc2NiNFVuX1k

4) Team

5) Questions

pbd2017[dot]1[at]gmail[dot]com