https://github.com/archismankarmakar/us-airlines-2015-dataset-sentiment-analysis

The US Airlines dataset (2015) contains 14,640 tweets (14,427 unique) collected between February 16-24, 2015, and covers 6 airlines with 15 data columns.
https://github.com/archismankarmakar/us-airlines-2015-dataset-sentiment-analysis

sentiment-analysis sentiment-classification twitter-sentiment-analysis

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The US Airlines dataset (2015) contains 14,640 tweets (14,427 unique) collected between February 16-24, 2015, and covers 6 airlines with 15 data columns.

• Host: GitHub
• URL: https://github.com/archismankarmakar/us-airlines-2015-dataset-sentiment-analysis
• Owner: ArchismanKarmakar
• Created: 2024-03-01T15:48:07.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-03-01T15:49:42.000Z (10 months ago)
• Last Synced: 2024-05-21T08:06:39.398Z (8 months ago)
• Topics: sentiment-analysis, sentiment-classification, twitter-sentiment-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 1.96 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SVM / Boosting / Decision Tree Twitter Sentiment Analysis on US Airlines 2015 Twitter Dataset

# Author: [Archisman Karmakar](https://www.github.com/ArchismanKarmakar)

#### This code is free to use and only for educational purposes, misuse of the code and dataset shall not be tolerated. Thanks.

### The US Airlines dataset (2015) contains 14,640 tweets (14,427 unique) collected between February 16-24, 2015, and covers 6 airlines with 15 data columns. We aim to explore the following research questions:

🟠 What is the relationship between airline sentiments and airlines? 


🟠 What is the reason for the negativity mentioned in the dataset? 


🟠 What is the relation of time with sentiments? 


🟠 Which model is best for sentiment analysis when we do ensemble learning?