A Python package for Deceptive Marketing Classification.
Project description
Deceptive Marketing Classification
A Python package for Deceptive Marketing Classification.
- Documentation: https://demarc.entelecheia.ai
- GitHub: https://github.com/entelecheia/demarc
- PyPI: https://pypi.org/project/demarc
This study aims to compare and analyze Naver Map reviews of restaurants that closed within a short period and those that achieved long-term success, in order to identify the characteristics of fake promotional reviews and develop a methodology for detecting them. We will apply Natural Language Processing (NLP) techniques using Large Language Models (LLMs) and representation vectors, and conduct an integrated analysis with rental car stay point data to investigate the relationship with actual business performance.
Changelog
See the CHANGELOG for more information.
Contributing
Contributions are welcome! Please see the contributing guidelines for more information.
License
This project is released under the MIT License.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file demarc-0.1.0.tar.gz
.
File metadata
- Download URL: demarc-0.1.0.tar.gz
- Upload date:
- Size: 4.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.10.0 readme-renderer/43.0 requests/2.31.0 requests-toolbelt/1.0.0 urllib3/2.2.1 tqdm/4.66.2 importlib-metadata/7.1.0 keyring/25.2.0 rfc3986/2.0.0 colorama/0.4.6 CPython/3.10.12
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0169269c301f5b4a7d347612853699d607fa0601b339161ad96fd1d2807cb395 |
|
MD5 | ecc4cf3b249ce2100c4d46154c1e7ff1 |
|
BLAKE2b-256 | f1c088fb8786798b66a0e60967943d85eb29a6937e9687b904036e66e6eda676 |
File details
Details for the file demarc-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: demarc-0.1.0-py3-none-any.whl
- Upload date:
- Size: 4.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.10.0 readme-renderer/43.0 requests/2.31.0 requests-toolbelt/1.0.0 urllib3/2.2.1 tqdm/4.66.2 importlib-metadata/7.1.0 keyring/25.2.0 rfc3986/2.0.0 colorama/0.4.6 CPython/3.10.12
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 47d2bd1428ae1ed8ce93930020fdaa49544e152f58f3fd7117be0feff470c1a4 |
|
MD5 | aae8491ea48aa2ee24b562741aa25c9a |
|
BLAKE2b-256 | f5cec8b4ca2651b78633f8c12623d0fd2ed009d3c1efdcce1ff4b082d82f6556 |