apurva-modi / pyspark-twitter-sentimental-analysis

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.

pyspark-twitter-sentimental-analysis

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.

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To run the notebook please follow these steps.

• 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.

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• 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.