{"id":28459685,"url":"https://github.com/sadegh-khedry/comments-sentiment-analysis","last_synced_at":"2026-04-04T08:42:07.905Z","repository":{"id":243008159,"uuid":"405974310","full_name":"Sadegh-Khedry/Comments-Sentiment-Analysis","owner":"Sadegh-Khedry","description":"text classification on comments using an ANN model.","archived":false,"fork":false,"pushed_at":"2024-08-22T11:50:39.000Z","size":12935,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-07T01:02:50.402Z","etag":null,"topics":["collections","deep-learning","keras","nlp","numpy","pandas","python","sentiment-analysis","sklearn","spacy","unicodedata"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Sadegh-Khedry.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-09-13T12:58:19.000Z","updated_at":"2024-08-22T11:50:42.000Z","dependencies_parsed_at":"2025-01-10T11:07:00.193Z","dependency_job_id":null,"html_url":"https://github.com/Sadegh-Khedry/Comments-Sentiment-Analysis","commit_stats":null,"previous_names":["sadegh15khedry/commentssentimentanalysis","sadegh15khedry/comments-sentiment-analysis","sadegh-khedry/comments-sentiment-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Sadegh-Khedry/Comments-Sentiment-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sadegh-Khedry%2FComments-Sentiment-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sadegh-Khedry%2FComments-Sentiment-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sadegh-Khedry%2FComments-Sentiment-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sadegh-Khedry%2FComments-Sentiment-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Sadegh-Khedry","download_url":"https://codeload.github.com/Sadegh-Khedry/Comments-Sentiment-Analysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sadegh-Khedry%2FComments-Sentiment-Analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263140443,"owners_count":23419881,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["collections","deep-learning","keras","nlp","numpy","pandas","python","sentiment-analysis","sklearn","spacy","unicodedata"],"created_at":"2025-06-07T01:02:49.813Z","updated_at":"2025-12-30T20:09:44.085Z","avatar_url":"https://github.com/Sadegh-Khedry.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Comments Sentiment Analysis\n\n\n## Introduction\nComments 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.\n\n## Table of Contents\n\n\n- [Directory Structure](#directory-structure)\n- [Files and Functions](#files-and-functions)\n- [Dataset](#dataset)\n- [Model Performance](#model-performance)\n- [Installation Guide](#installation-guide)\n- [Acknowledgments](#acknowledgments)\n- [Further Improvements](#further-improvements)\n- [License](#license)\n\n\n## Directory Structure\n```\n├── src\n│ ├── utils.py\n│ ├── model_training.py\n│ ├── model_evaluation.py\n│ ├── data_preprocessing.py\n│ └── data_exploration.py\n├── notebooks\n│ ├── data_exploration.ipynb\n│ ├── data_preprocessing.ipynb\n│ ├── model_training.ipynb\n│ └── model_evaluation.ipynb\n├── environment.yml\n└── README.md\n```\n\n\n## Files and Functions\n\n- `utils.py` : Utility functions for various tasks.\n- `model_training.py` : Functions for training the model.\n- `model_evaluation.py` : Functions for evaluating the model.\n- `data_preprocessing.py` : Functions for data preprocessing.\n- `data_exploration.py` : Functions for data exploration.\n- `data_exploration.ipynb`: Notebook for data exploration.\n- `data_preprocessing.ipynb`: Notebook for data preprocessing.\n- `model_training.ipynb`: Notebook for model training.\n- `model_evaluation.ipynb`: Notebook for model evaluation.\n\n\n## Dataset\n\nThe 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\n\n## Model Performance\n\n### Train\n- loss: 0.3185\n- accuracy: 0.9040\n  \n### Validataion\n- loss: 0.4368\n- accuracy: 0.8587\n  \n### Test\n\n```\n              precision    recall  f1-score   support\n\n           0       0.87      0.85      0.86       376\n           1       0.85      0.88      0.86       374\n\n    accuracy                           0.86       750\n   macro avg       0.86      0.86      0.86       750\nweighted avg       0.86      0.86      0.86       750\n```\n-  test_loss: 0.44\n- accuracy: 0.86\n- precision: 0.86\n- recall: 0.86\n- f1: 0.86\n  \n## Installation Guide\n\nTo set up the project environment, use the `environment.yml` file to create a conda environment.\n\n1. **Clone the repository:**\n\n    ```bash\n    git clone https://github.com/sadegh15khedry/Comments-Sentiment-Analysis.git\n    cd Comments-Sentiment-Analysis\n    ```\n\n2. **Create the conda environment:**\n\n    ```bash\n    conda env create -f environment.yml\n    ```\n\n3. **Activate the conda environment:**\n\n    ```bash\n    conda activate comments\n    ```\n\n4. **Verify the installation:**\n\n    ```bash\n    python --version\n    ```\n\n\n## Acknowledgments\n- Special thanks to the developers and contributors the libraries used in this project, including NumPy, pandas, scikit-learn, Seaborn, and Matplotlib.\n- Huge thaks to contributors of the IMDB Dataset.\n\n## Further Improvements\n\n- more hyperparameter tuning to optimize the model parameters.\n\n\n  \n## License\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsadegh-khedry%2Fcomments-sentiment-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsadegh-khedry%2Fcomments-sentiment-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsadegh-khedry%2Fcomments-sentiment-analysis/lists"}