{"id":26779126,"url":"https://github.com/shallowmanica/covid19-sentiment-analysis","last_synced_at":"2026-04-08T16:31:50.616Z","repository":{"id":285004459,"uuid":"956762887","full_name":"shallowManica/COVID19-Sentiment-Analysis","owner":"shallowManica","description":"A deep learning project that classifies COVID-19 tweet sentiments using CNN, ANN, and LSTM models. The project uses the manually tagged \"Coronavirus tweets NLP - Text Classification\" dataset from Kaggle to analyze public sentiment during the pandemic.","archived":false,"fork":false,"pushed_at":"2025-03-28T21:23:08.000Z","size":7289,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-28T21:32:28.807Z","etag":null,"topics":["cnn","lstm","nltk","pytorch","rnn","sentiment-analysis","sklearn","tensorflow","text-classification"],"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/shallowManica.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":"2025-03-28T20:21:31.000Z","updated_at":"2025-03-28T21:23:12.000Z","dependencies_parsed_at":"2025-03-28T21:42:34.240Z","dependency_job_id":null,"html_url":"https://github.com/shallowManica/COVID19-Sentiment-Analysis","commit_stats":null,"previous_names":["shallowmanica/covid19-sentiment-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/shallowManica/COVID19-Sentiment-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shallowManica%2FCOVID19-Sentiment-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shallowManica%2FCOVID19-Sentiment-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shallowManica%2FCOVID19-Sentiment-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shallowManica%2FCOVID19-Sentiment-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shallowManica","download_url":"https://codeload.github.com/shallowManica/COVID19-Sentiment-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shallowManica%2FCOVID19-Sentiment-Analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266624763,"owners_count":23958300,"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","status":"online","status_checked_at":"2025-07-23T02:00:09.312Z","response_time":66,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["cnn","lstm","nltk","pytorch","rnn","sentiment-analysis","sklearn","tensorflow","text-classification"],"created_at":"2025-03-29T06:14:52.648Z","updated_at":"2026-04-08T16:31:50.575Z","avatar_url":"https://github.com/shallowManica.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# COVID-19 Tweet Sentiment Analysis Using CNN, RNN and LSTM\n\n## Overview\nThis repository contains a Jupyter Notebook that demonstrates sentiment analysis on COVID-19-related tweets using deep learning techniques. The goal is to classify tweets into sentiment categories (e.g., Negative, Positive, Other) by exploring and comparing three deep learning architectures:\n- **Convolutional Neural Network (CNN)**\n- **Artificial Neural Network (ANN)**\n- **Long Short-Term Memory (LSTM) Network**\n\nThese models help reveal patterns in public sentiment during the COVID-19 pandemic.\n\n## Dataset\nThe dataset is sourced from [Kaggle: Coronavirus tweets NLP - Text Classification](https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification). It comprises tweets related to the COVID-19 pandemic that have been manually tagged for sentiment. Key columns in the dataset include:\n- **Location:** The origin of the tweet.\n- **Tweet At:** Timestamp of when the tweet was posted.\n- **Original Tweet:** The text content of the tweet.\n- **Label:** The manually assigned sentiment (e.g., Negative, Positive, Other).\n\n## Features\n- **Data Preprocessing:** Clean and tokenize tweet texts to prepare for model input.\n- **Modeling:** Implementation and comparative analysis of three models:\n  - **CNN:** To capture local features from text sequences.\n  - **ANN:** A baseline deep learning model for text classification.\n  - **LSTM:** To capture sequential dependencies and context in tweets.\n- **Evaluation \u0026 Visualization:** Analyze model performance using accuracy metrics and visualize sentiment distribution and trends.\n\n## Requirements\n- Python 3.x\n- Jupyter Notebook or JupyterLab\n\nRequired Python packages:\n- pandas\n- numpy\n- nltk\n- tensorflow (or keras)\n- matplotlib\n- seaborn\n\n## Installation \u0026 Setup\n1. **Clone the Repository:**\n   ```bash\n   git clone https://github.com/YourUsername/COVID19-Sentiment-Analysis.git\n   ```\n2. **Navigate to the Project Directory:**\n   ```bash\n   cd COVID19-Sentiment-Analysis\n   ```\n\n## Usage\n- **Launch Jupyter Notebook:**\n  ```bash\n  jupyter notebook\n  ```\n- Open the `sentiment_analy.ipynb` notebook and run the cells sequentially to preprocess the data, train the models, and visualize the results.\n\n## File Structure\n- `sentiment_analy.ipynb`: The main notebook with the complete sentiment analysis workflow.\n- `data/`: Directory to store the COVID-19 tweets dataset (or instructions to download it from Kaggle).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshallowmanica%2Fcovid19-sentiment-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshallowmanica%2Fcovid19-sentiment-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshallowmanica%2Fcovid19-sentiment-analysis/lists"}