https://github.com/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.
https://github.com/apurva-modi/pyspark-twitter-sentimental-analysis

airline pyspark-mllib reviews sentimental-analysis twitter

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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.

• Host: GitHub
• URL: https://github.com/apurva-modi/pyspark-twitter-sentimental-analysis
• Owner: apurva-modi
• Created: 2020-04-29T21:40:54.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-01T02:13:33.000Z (over 4 years ago)
• Last Synced: 2023-07-24T00:25:22.654Z (over 1 year ago)
• Topics: airline, pyspark-mllib, reviews, sentimental-analysis, twitter
• Language: Jupyter Notebook
• Homepage:
• Size: 406 KB
• Stars: 3
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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.