This repository contains a jupyter notebook and the necessary data to implement sentiment analysis of tweets using Logistic Regression. Please open the notebook for more information.
The dataset was obtained from a Kaggle competition. The dataset is divided into a train and a test dataset. Each record contains the following fields:
| Field name | Meaning |
|---|---|
| ItemID | id of twit |
| Sentiment | sentiment (1-positive, 0-negative) |
| SentimentText | text of the twit |
You can go straight ahead and try out the algorithm with a small web app I have included in this repository, just run:
cd site
python app.py
Then open a browser in the default address (http://127.0.0.1:5000/) and play around:
This notebook will run in Python >= 3.5. The following packages are required:
- bokeh
- flask
- nltk
- numpy
- pandas
- scikit-learn
Because the training set contains only English twits, this classifier will only work with English twits.