To Analyze how travelers expressed their feelings on Twitter using pyspark MLlib. Given tweets about six US airlines, the task is to predict whether a tweet contains positive, negative, or neutral sentiment about the airline. This is a typical supervised learning task where given a text string, I have to categorize the text string into predefined categories.
- Clone the project.
- Install Docker
Mac: https://docs.docker.com/docker-for-mac/install/ Windows: https://docs.docker.com/docker-for-windows/install/r
- Browse to the folder path using terminal
$docker-compose up
- Then open the the Url which looks something like this http://127.0.0.1:8888/`
- By copying it from the terminal screen and pasting it to browser.
It provides an interactive Jupyter Notebook environment, open the Sentimental_Analysis.ipyb and execute it cell by cell.
- You can just open Sentimental_Analysis.ipynb in the Jupyter server and then see the output, but you will to be able to execute it cell by cell.