============================================== App Store Review Anaysis
In this research project we extract mobile applications' information and their user reviews from iTunes and then using document similarity methods measure the contribution of users' feedback to app success.
Requirements
To install all the python packages inside a virtualenv or as root (sudo):
pip install -r requirements.txt
To complete the installation for NLTK and Stanford Parser see below.
If you prefer a step-by-step installation see below.
General
sudo apt-get install python-pip python-dev
Numpy, Scipy, Pandas, and matplotlib
sudo apt-get install build-essential gfortran libatlas-base-dev
sudo pip install --upgrade pip
sudo pip install numpy
sudo pip install scipy
sudo pip install matplotlib
sudo pip install pandas
Django
sudo pip install Django==1.9.7
PostgreSQL adapter for the Python
sudo pip install psycopg2
Python driver for MongoDB
sudo pip install pymongo
lxml
sudo pip install lxml
NLTK
sudo pip install nltk
import nltk
nltk.download()
gensim
sudo pip install --upgrade gensim
sklearn
sudo pip install -U scikit-learn
XlsxWriter
sudo pip install XlsxWriter
Optional
PiCloud
sudo pip install cloud
TextBlob
sudo pip install -U textblob
TensorFlow and Six
https://www.tensorflow.org/versions/r0.9/get_started/os_setup.html
Java JRE
http://askubuntu.com/questions/521145/how-to-install-oracle-java-on-ubuntu-14-04
sudo apt-add-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer
Stanford Parser (2015-04-20, which is compatible with NLTK)
http://nlp.stanford.edu/software/stanford-parser-full-2015-04-20.zip
path = '/home/kaminem64/stanford'
os.environ['CLASSPATH'] = '%s/stanford-postagger-full-2015-04-20/stanford-postagger.jar:%s/stanford-ner-2015-04-20/stanford-ner.jar:%s/stanford-parser-full-2015-04-20/stanford-parser.jar:%s/stanford-parser-full-2015-04-20/stanford-parser-3.6.0-models.jar' %(path, path, path, path)
os.environ['STANFORD_MODELS'] = '%s/stanford-postagger-full-2015-04-20/models:%s/stanford-ner-2015-04-20/classifiers' %(path, path)
Setup Database
python manage.py makemigrations
python manage.py migrate
Download App Details and Reviews
python app/run_crawler.py
Download App Rankings
python rankings/get_rankings.py
Process
Download Rankings -> Analyze Rankings -> Download Reviews -> Download All Release Notes -> Create a Flat DB -> Create LSA or LDA Model -> Calculate Similarities -> Create Panel Data