{"id":18710844,"url":"https://github.com/sayamalt/twitter-sentiment-analysis","last_synced_at":"2025-11-09T14:30:18.662Z","repository":{"id":133945282,"uuid":"549814699","full_name":"SayamAlt/Twitter-Sentiment-Analysis","owner":"SayamAlt","description":"Successfully established a machine learning model which can accurately classify the sentiment of any particular tweet into either positive, negative or neutral category.","archived":false,"fork":false,"pushed_at":"2022-11-13T10:30:16.000Z","size":9859,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-28T08:09:41.674Z","etag":null,"topics":["data-visualization","exploratory-data-analysis","nlp","sentiment-analysis","supervised-learning","text-processing"],"latest_commit_sha":null,"homepage":"","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/SayamAlt.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":"2022-10-11T19:29:21.000Z","updated_at":"2022-10-11T19:41:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"95925f40-a9b1-4eaf-88b2-a23ada572c1b","html_url":"https://github.com/SayamAlt/Twitter-Sentiment-Analysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FTwitter-Sentiment-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FTwitter-Sentiment-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FTwitter-Sentiment-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayamAlt%2FTwitter-Sentiment-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SayamAlt","download_url":"https://codeload.github.com/SayamAlt/Twitter-Sentiment-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239576517,"owners_count":19662109,"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":["data-visualization","exploratory-data-analysis","nlp","sentiment-analysis","supervised-learning","text-processing"],"created_at":"2024-11-07T12:35:57.404Z","updated_at":"2025-11-09T14:30:18.472Z","avatar_url":"https://github.com/SayamAlt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Twitter-Sentiment-Analysis\nSuccessfully established a machine learning model which can accurately classify the sentiment of any particular tweet into either positive, negative or neutral category.\n\n![Twitter Sentiment Analysis](https://i.ytimg.com/vi/ujId4ipkBio/maxresdefault.jpg)\n![Twitter Sentiment Analysis](https://i.ytimg.com/vi/pgZcP852dMg/maxresdefault.jpg)\n\n## Dataset Used\n\nLink: https://www.kaggle.com/datasets/cosmos98/twitter-and-reddit-sentimental-analysis-dataset\n\n## Context\n\nThe dataset was created as part of a university project on Sentimental Analysis On Multi-Source Social Media Platforms using PySpark. It comprises tweets from Twitter along with their sentimental label.\n\n## Content\n\nThe dataset contains about 163K tweets along with their respective sentiment labels. Overall, the dataset consists of 2 columns, the first column has the cleaned tweets and the second one indicates its sentimental label.\n\n## Technologies Used\n\n\u003cul\u003e\n  \u003cli\u003eNumpy\u003c/li\u003e\n  \u003cli\u003ePandas\u003c/li\u003e\n  \u003cli\u003eSeaborn\u003c/li\u003e\n  \u003cli\u003eMatplotlib\u003c/li\u003e\n  \u003cli\u003eNLTK\u003c/li\u003e\n  \u003cli\u003eSymSpellPy\u003c/li\u003e\n  \u003cli\u003eCatBoost\u003c/li\u003e\n  \u003cli\u003eScikit-learn\u003c/li\u003e\n  \u003cli\u003eLightGBM\u003c/li\u003e\n\u003c/ul\u003e\n\n## Acknowledgements\n\nThis Dataset was created with the help of Tweepy Apis. \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayamalt%2Ftwitter-sentiment-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayamalt%2Ftwitter-sentiment-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayamalt%2Ftwitter-sentiment-analysis/lists"}