Metadata-Version: 2.1
Name: IQS-algorithm
Version: 1.0.0
Summary: The IQS is an iterative approach for optimizing short keyword queries given a prototype document through interaction with an opaque search engine such as Twitter.
Home-page: https://iqs.cs.bgu.ac.il/
Author: Mr. Maor Reuben and Dr. Aviad Elishar
Author-email: iqs.bgu@gmail.com
Project-URL: Academic Article, https://www.sciencedirect.com/science/article/pii/S0957417422004432
Project-URL: Patent, https://patents.google.com/patent/US20200327120A1/en?inventor=Maor+reuben&oq=Maor+reuben
Classifier: Programming Language :: Python :: 3
Classifier: License :: Free for non-commercial use
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pathlib
Requires-Dist: nltk
Requires-Dist: tweepy
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: gensim ==3.8.3
Requires-Dist: uuid
Requires-Dist: tqdm
Requires-Dist: requests
Requires-Dist: importlib-resources ==1.3.0
Requires-Dist: python-dotenv

# Iterative Query Selection (IQS)

## Python Package Overview

* Performs the IQS algorithm on various queries by providing a simple API for accessing all its functionality.
* Modify the quality of the search results from Twitter by setting different parameters.
* Designed for users with technical background.
* Download the package using pip install IQS-algorithm

## Check out the IQS algorithm web platform in the following link: [IQS Web](https://iqs.cs.bgu.ac.il/)

The platform can help you explore the IQS algorithm benefits:
* Search and retrieve data from Twitter's website using the IQS algorithm.
* Displays results from the experiment described in the academic paper. In the experiment, the IQS algorithm is compared to another algorithm in the field (ALMIK) on a specific dataset.
* Presenting the academic paper with Q&A
