The goal of this project is to extract useful information such as sentiment and topic from individual sentences instead of entire documents. This is achieved without the need for pre-labeled data, leveraging unsupervised machine learning algorithms. The extracted data can be used to create adaptive settings with stage effects, sound effects, and graphics to enhance viewer experiences in stage and theater settings. This software also provides a web interface that simulates these effects.
- Sentiment Analysis: Detects the sentiment (positive or negative) of sentences.
- Topic Recognition: Identifies the main topic of sentences.
- Unsupervised Learning: Operates without the need for labeled training data.
- Adaptive Settings: Utilizes sentiment and topic data to influence stage and theater settings.
- Web Interface: A user-friendly web interface to demonstrate the capabilities of the software.
To run this project, you need to have the following dependencies installed:
djangodjango rest frameworknltkgensimpandasnumpy
Follow these instructions to get a copy of the project up and running on your local machine.
Ensure you have Docker installed on your system.
-
Clone the repository:
git clone https://github.com/sbaglieri13/TextAnalysis.git cd TextAnalysis -
Build the Docker image:
docker build -t webapp . -
Run the Docker container:
docker run -d -p 8000:8000 webapp
Once the Docker container is running, access the web interface to start analyzing text for sentiment and topics.
The web interface provides visual feedback based on the analyzed sentiment and topic of the input text.
I welcome feedback and contributions from the community. If you have any suggestions or would like to contribute to the project, please open an issue or submit a pull request on my GitHub repository.