This is a simple web application built using Python's Flask web framework that allows users to perform sentiment analysis on tweets related to a specific topic within a given date range. The application is built using two machine learning models - one for emotion analysis and another for sentiment analysis.
To use this application, follow the steps below:
- Clone this repository to your local machine using
git clone https://github.com/<username>/<repository>.git - Navigate to the project directory using
cd <repository> - Install the required packages using
pip install -r requirements.txt
- Run the application using
python app.pyand navigate tohttp://localhost:5000in your web browser. - Enter a topic in the first textbox, the number of tweets to scrape in the second textbox, and the date range in the third and fourth textboxes.
- Click the 'Submit' button to initiate the sentiment analysis.
- The results of the analysis will be displayed in a csv, containing the probabilities of each emotion and sentiment class for the tweets.
Before running the Flask application, you need to train the machine learning models on the dataset. This can be done by running the following notebooks:
- Sentimental Analysis - Part 1.ipynb
- Emotional Analysis - Part 2.ipynb
After running these notebooks, the following files will be generated and you should have them in the static folder before running the application:
- my_model.h5
- sentimentmodel.pkl
- tokenizer.pickle
- vectorizer.pickle
The dataset used in this project comes from the following resources:
Once the models are trained, you can run the Flask application to perform sentiment analysis on the tweets related to a specific topic.
This application requires the following dependencies:
- Flask==2.1.0
- GetOldTweets3==0.0.11
- numpy==1.19.5
- pandas==1.3.4
- scikit-learn==1.0.2
- tensorflow==2.6.0