Hactober hactober oo aa oo aa
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The project consists of three stages:
- Data Processing
- Model Definition
- Model training, and evaluation
The .ipynb (Jupyter Notebook) file contains the three stages of the project.
Data Processing refers to the analysis, manipulation/transformation of the dataset to obtain a usable form of the data.
This consists of data partitioning, tokenization, padding, etc.
Model definition refers to the process of choosing the models those are best suited for the dataset, along with the initial hyperparameters.
Model training refers to the process of fitting the models to the training data.
Model evaluation refers to the process of evaluating the process of the models using certain appropriate evaluation metrics and tuning the hyperparameters again based on the results.
It is an iterative process until required performance is obtained.
This model can be used to analyse the sentiment in a given piece of text. This has its various applications such as:
- Spam Classification
- Business Analytics
- Social Monitoring
- Market Research
- Product Analysis
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Soundar Murugan - @soundarzozm
E-Mail - soundarmurugan91@gmail.com
Project Link: https://github.com/soundarzozm/hactoberrrrr
- Mayank Mandhani for the repo.