PeerLogic Web Services: Peer-reviews-NLP: Difference between revisions
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==Summary== | |||
The Peer Review NLP Web Service (PRWS) utilizes natural language processing to perform sentiment, volume, and emotion analysis upon student peer reviews. | The Peer Review NLP Web Service (PRWS) utilizes natural language processing to perform sentiment, volume, and emotion analysis upon student peer reviews. | ||
The service was created with the intention of being set up using Docker and with the HTTP server created using Gunicorn. | The service was created with the intention of being set up using Docker and with the HTTP server created using Gunicorn. | ||
==Components== | |||
The project features the following primary directories: | The project features the following primary directories: | ||
* data: A storage space for tagged comments. Utilized for X. | * data: A storage space for tagged comments. Utilized for X. | ||
Revision as of 00:53, 24 January 2022
Summary
The Peer Review NLP Web Service (PRWS) utilizes natural language processing to perform sentiment, volume, and emotion analysis upon student peer reviews.
The service was created with the intention of being set up using Docker and with the HTTP server created using Gunicorn.
Components
The project features the following primary directories:
- data: A storage space for tagged comments. Utilized for X.
- documentation: A number of Swagger-supported .yml files which outline the web service's API.
- metrics: A number of files which handle the input and output from the webservice, reading JSON input and returning it using different methods.
- model: Several non-human-readable files representing the compiled models utilized by the web service.
- preprocessing: The primary "runner" files which perform the actual prediction associated with the web service.
- templates: Contains a single, non-functional HTML page apparently meant for testing the NLP.
In addition, it also contains a number of files associated with initial start up. Each of these sections will be discussed independently.