The purpose of this project is to make up a prediction model where we will be able to predict whether a recommendation is positive or negative. In this project, we will not focus on the Score, but only the positive/negative sentiment of the recommendation.
To do so, we will work on Amazon's recommendation dataset, we will build a Term-doc incidence matrix using term frequency and inverse document frequency ponderation. When the data is ready, we will load it into predicitve algorithms, mainly naïve Bayesian and regression.
Dataset can be downloaded from this link. After downloading the data copy it to the 'resources' folder.
The application can be run in one of the two ways, either using Python Interpreter or using Jupyter Notebook.
Python Environment
This project require
Python 3.6 interpreter.
To use the Python interpreter to run the project, first install the python packages being used in this project.
pip3 install -r requirements.txt
or
pip install -r requirements.txt
To run the application
$python P3
Conda Environment
The project requires Anaconda 3.
To install Anaconda3 download the shell script from Anaconda website.
Run the following command
bash Anaconda-latest-Linux-x86_64.sh
Run the following to create environment and install packages
conda env create -f project_env.yml
Run the following to run the Jupyter Notebook
jupyter notebook
Select the '.ipynb' file.