Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sadegh15khedry/comments-sentiment-analysis
text classification on comments using an ANN model.
https://github.com/sadegh15khedry/comments-sentiment-analysis
collections deep-learning keras nlp numpy pandas python sentiment-analysis sklearn spacy unicodedata
Last synced: about 1 month ago
JSON representation
text classification on comments using an ANN model.
- Host: GitHub
- URL: https://github.com/sadegh15khedry/comments-sentiment-analysis
- Owner: sadegh15khedry
- License: mit
- Created: 2021-09-13T12:58:19.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-08-22T11:50:39.000Z (4 months ago)
- Last Synced: 2024-08-22T14:35:23.843Z (4 months ago)
- Topics: collections, deep-learning, keras, nlp, numpy, pandas, python, sentiment-analysis, sklearn, spacy, unicodedata
- Language: Jupyter Notebook
- Homepage:
- Size: 12.3 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Comments Sentiment Analysis
## Introduction
Comments Sentiment Analysis is a project focused on analyzing the sentiment of user comments. It utilizes natural language processing (NLP) techniques to classify comments as positive, negative, or neutral. This project aims to provide insights into user opinions and feedback by automatically categorizing the sentiment of their comments.
## Table of Contents
- [Directory Structure](#directory-structure)
- [Files and Functions](#files-and-functions)
- [Dataset](#dataset)
- [Model Performance](#model-performance)
- [Installation Guide](#installation-guide)
- [Acknowledgments](#acknowledgments)
- [Further Improvements](#further-improvements)
- [License](#license)
## Directory Structure
```
├── src
│ ├── utils.py
│ ├── model_training.py
│ ├── model_evaluation.py
│ ├── data_preprocessing.py
│ └── data_exploration.py
├── notebooks
│ ├── data_exploration.ipynb
│ ├── data_preprocessing.ipynb
│ ├── model_training.ipynb
│ └── model_evaluation.ipynb
├── environment.yml
└── README.md
```
## Files and Functions
- `utils.py` : Utility functions for various tasks.
- `model_training.py` : Functions for training the model.
- `model_evaluation.py` : Functions for evaluating the model.
- `data_preprocessing.py` : Functions for data preprocessing.
- `data_exploration.py` : Functions for data exploration.
- `data_exploration.ipynb`: Notebook for data exploration.
- `data_preprocessing.ipynb`: Notebook for data preprocessing.
- `model_training.ipynb`: Notebook for model training.
- `model_evaluation.ipynb`: Notebook for model evaluation.
## Dataset
The dataset used is the imdb comment Dataset. get the dataset using the fallowing link https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews
## Model Performance
### Train
- loss: 0.3185
- accuracy: 0.9040
### Validataion
- loss: 0.4368
- accuracy: 0.8587
### Test
```
precision recall f1-score support
0 0.87 0.85 0.86 376
1 0.85 0.88 0.86 374
accuracy 0.86 750
macro avg 0.86 0.86 0.86 750
weighted avg 0.86 0.86 0.86 750
```
- test_loss: 0.44
- accuracy: 0.86
- precision: 0.86
- recall: 0.86
- f1: 0.86
## Installation Guide
To set up the project environment, use the `environment.yml` file to create a conda environment.
1. **Clone the repository:**
```bash
git clone https://github.com/sadegh15khedry/Comments-Sentiment-Analysis.git
cd Comments-Sentiment-Analysis
```
2. **Create the conda environment:**
```bash
conda env create -f environment.yml
```
3. **Activate the conda environment:**
```bash
conda activate comments
```
4. **Verify the installation:**
```bash
python --version
```
## Acknowledgments
- Special thanks to the developers and contributors the libraries used in this project, including NumPy, pandas, scikit-learn, Seaborn, and Matplotlib.
- Huge thaks to contributors of the IMDB Dataset.
## Further Improvements
- more hyperparameter tuning to optimize the model parameters.
## License
This project is licensed under the MIT License. See the LICENSE file for details.