This case example was done within the scope of the seminar paper "Social Media Analytics" for the 2018/2019 Data Warehouse Systems Seminar (703615 SE/2 SE) by Univ.-Prof. Mag. Dr. Maier Ronald
The case example was developed using python and jupyter along with several python libraries that can be found in the requirements.txt file. The main analysis can be found in the twitter-airline-sentiment-analysis.ipynb notebook.
Update 21-01-2019
The twitter-airline-sentiment-analysis-vader-comparison.ipynb notebook was included to show a comparison of human verified sentiment classification to VADER classification.
Default python3 and pip are required to run this notebook.
Set up a .venv virtual environment
python3 -m venv .venv
Activate the .venv virtual environment
. .venv/bin/activate
Install the requirements defined in requirements.txt
pip install -r requirements.txt
Start jupyter and open twitter-airline-sentiment-analysis.ipynb
jupyter notebook
- Twitter US Airline Sentiment - Analyze how travelers in February 2015 expressed their feelings on Twitter
- Based on an article from the data scientist Martín Pellarolo
- Crowdflower