{"id":20037066,"url":"https://github.com/abhiiiman/twitter_sentiment_analysis","last_synced_at":"2026-05-13T09:32:24.938Z","repository":{"id":245136931,"uuid":"817393822","full_name":"abhiiiman/Twitter_Sentiment_Analysis","owner":"abhiiiman","description":"Predict the sentiments of the Twitter tweets in a go using NLP techniques and Logistic Regresion Model. ","archived":false,"fork":false,"pushed_at":"2024-06-19T16:33:12.000Z","size":9629,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-12T18:51:53.860Z","etag":null,"topics":["deployed","nlp","nlp-machine-learning","render","sentiment","streamlit","twitter","twitter-sentiment-analysis"],"latest_commit_sha":null,"homepage":"https://twitter-sentiment-analysis-e1b1.onrender.com/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/abhiiiman.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-06-19T15:57:08.000Z","updated_at":"2024-06-19T16:36:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"dd1c6fa2-b49a-4ae3-bd0b-5aba1c3cf93a","html_url":"https://github.com/abhiiiman/Twitter_Sentiment_Analysis","commit_stats":null,"previous_names":["abhiiiman/twitter_sentiment_analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abhiiiman%2FTwitter_Sentiment_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abhiiiman%2FTwitter_Sentiment_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abhiiiman%2FTwitter_Sentiment_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abhiiiman%2FTwitter_Sentiment_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/abhiiiman","download_url":"https://codeload.github.com/abhiiiman/Twitter_Sentiment_Analysis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241466527,"owners_count":19967477,"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":["deployed","nlp","nlp-machine-learning","render","sentiment","streamlit","twitter","twitter-sentiment-analysis"],"created_at":"2024-11-13T10:18:08.116Z","updated_at":"2026-05-13T09:32:24.879Z","avatar_url":"https://github.com/abhiiiman.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Twitter Sentiment Analysis 🐦😃☹️\n\n### This project leverages _Natural Language Processing_ __(NLP)__ and _Logistic Regression_ to classify the sentiment of tweets as either `positive` or `negative`. The model achieved an accuracy of `82%`. Below you'll find detailed instructions on how to set up and run this project locally, as well as how to use the deployed `Streamlit` app.\n\n# Project Structure\n\u003c!-- Twitter_Sentiment_Analysis\u003cbr\u003e\n│\u003cbr\u003e\n├── Data\u003cbr\u003e\n│   └── dataset.csv\u003cbr\u003e\n│\u003cbr\u003e\n├── Models\u003cbr\u003e\n│   ├── Twitter_Model.sav\u003cbr\u003e\n│   └── Vectorizer.pkl\u003cbr\u003e\n│\u003cbr\u003e\n├── app.py\u003cbr\u003e\n├── README.md\u003cbr\u003e\n└── Twitter_Sentiment_Analysis.ipynb\u003cbr\u003e --\u003e\n\u003c!-- ![alt text](Screenshots\\image2.png) --\u003e\n\u003cp\u003e\n    \u003cimg src=\"Screenshots\\image2.png\"/\u003e\n\u003c/p\u003e\n\n\n## Setup Instructions\n\n1. **Clone the Repository**\n\n```html\ngit clone https://github.com/abhiiiman/Twitter_Sentiment_Analysis.git\n```\n\n```html\ncd Twitter_Sentiment_Analysis\n```\n\n2. **Create a Virtual Environment**\n\n```html\npython -m venv venv\n```\n- Mac Users\n```html\nsource venv/bin/activate \n```\n- Windows Users\n```html\nvenv\\Scripts\\activate\n```\n\n3. **Install Dependencies**\n\n```html\npip install -r requirements.txt\n```\n\n4. **Download NLTK Data**\n\n- In a Python shell, run:\n\n```python\nimport nltk\nnltk.download('stopwords')\nnltk.download('punkt')\n```\n\n5. **Download the Dataset from here**\n[Download the Dataset](https://www.kaggle.com/datasets/kazanova/sentiment140)\n\n6. **Run the Streamlit App**\n\n```html\nstreamlit run app.py\n```\n\n# Using the Deployed Streamlit App\n1. Navigate to the Streamlit App [Click Here](https://twitter-sentiment-analysis-e1b1.onrender.com/)\n2. Enter Tweet Content\n3. Predict Sentiment\n4. Screenshots\n- Negative Tweet\n\n\u003c!-- ![alt text](Screenshots\\image.png) --\u003e\n\u003cp\u003e\n    \u003cimg src=\"Screenshots\\image.png\"/\u003e\n\u003c/p\u003e\n\n- Positive Tweet\n\n\u003c!-- ![alt text](Screenshots\\image-1.png) --\u003e\n\u003cp\u003e\n    \u003cimg src=\"Screenshots\\image-1.png\"/\u003e\n\u003c/p\u003e\n\n# Don't forget to give it a Star!\n\n## _If you loved this project, give it a_ ⭐ _on GitHub! It would make my codebase as happy as a positive tweet_ 😄.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhiiiman%2Ftwitter_sentiment_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhiiiman%2Ftwitter_sentiment_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhiiiman%2Ftwitter_sentiment_analysis/lists"}