Metadata-Version: 2.1
Name: sentiment-analysis-csci-e89
Version: 0.0.1
Summary: Package for end to end setiment analysis using Neural Architectures
Home-page: https://github.com/stefano10p/sentiment_analysis_csci_e89
Author: Stefano Parravano
Author-email: stefanoparravano10@gmail.com
License: MIT
Description: # sentiment_analysys_csci_e89
        
        This package was designed to enable its users to perform end to end sentiment analysis with state of the art techniques.  
        The api assumes a common data model that is described in great detail in the
        documentation. In short, the modules expect tabular datasets with the following fields for training data:
         1. text_id    
         2. text    
         3. label    
        
         and the folling fields for live test data:  
         1. text_id    
         2. text    
        
        The api contains 5 main modules:    
         1. data_cleaning: A class that was written to support a number of popular machine learning datasets. It cleans the raw data and
         structures it in a way that the other modules can use.  
         2. pre_processing : A class that provides a number of high level functions to perform sophisticated data transformations and cleaning.    This class is responsible for preparing the raw text data for our Neural architectures.    
         3. modeling : A class that provides a number of methods each dedicated to training a certain type of architecture. Refer to the documentation for the eaxact specification of each of the architectures provided.  
         4. pretrained_embeddings : A class that provides methods to prepare well known and popular word embeddings (GloVe adn word2vec) in a format that our netoworks can work with. We require that the user download the raw data from the appropriate sources.Once again, details are included in the documentation.  
         5. predict_newdata: A class that provides methods to use our trained networks to make prediction on live data. live data as I define it here can be thought of test data that is processed and prepared outside of the original efforts that processed the data our model was trained and validated against.  
        
         A number of different neural architectures are provided with easy to call methods, thereby allowing you to train sophisticated models with no more than a few lines of code.Some of the architectures implement transfer learning and require that certain files be downloaded
         locally.  
        
         Please refer to the documentation anf the tutorial script  
        
         ## Installation  
         Run the following to install:  
        
         ```python
         pip install setiment_analysis_csci_e89
         ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: Implementation
Description-Content-Type: text/markdown
