{"id":17673933,"url":"https://github.com/pratishtha-abrol/sentimentanalysis","last_synced_at":"2026-05-17T08:33:19.677Z","repository":{"id":118596161,"uuid":"268560982","full_name":"pratishtha-abrol/SentimentAnalysis","owner":"pratishtha-abrol","description":"Logistic Regression: A sentiment analysis case study","archived":false,"fork":false,"pushed_at":"2020-06-01T17:05:35.000Z","size":25906,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-12T20:44:46.436Z","etag":null,"topics":["logistic-regression","nltk-python","scikit-learn","sentiment-analysis"],"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/pratishtha-abrol.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":"2020-06-01T15:32:15.000Z","updated_at":"2021-03-04T03:37:44.000Z","dependencies_parsed_at":null,"dependency_job_id":"f26ec78c-26d3-4508-b04f-fca1eb401819","html_url":"https://github.com/pratishtha-abrol/SentimentAnalysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pratishtha-abrol/SentimentAnalysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pratishtha-abrol%2FSentimentAnalysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pratishtha-abrol%2FSentimentAnalysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pratishtha-abrol%2FSentimentAnalysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pratishtha-abrol%2FSentimentAnalysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pratishtha-abrol","download_url":"https://codeload.github.com/pratishtha-abrol/SentimentAnalysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pratishtha-abrol%2FSentimentAnalysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33131931,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T06:27:06.342Z","status":"ssl_error","status_checked_at":"2026-05-17T06:26:59.432Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["logistic-regression","nltk-python","scikit-learn","sentiment-analysis"],"created_at":"2024-10-24T06:22:33.088Z","updated_at":"2026-05-17T08:33:19.645Z","avatar_url":"https://github.com/pratishtha-abrol.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SentimentAnalysis\nLogistic Regression: A sentiment analysis case study\n\n## Key Concepts\n* Build and employ a logistic regression classifier using scikit-learn\n* Clean and pre-process data\n* Perform feature extraction with nltk\n* Tune model hyperparameters and evaluate model accuracy\n\n## Dataset\nIMDB movie reviews dataset\nhttp://ai.stanford.edu/~amaas/data/sentiment\nContains 25000 positive and 25000 negative reviews \nContains at most reviews per movie\nAt least 7 stars out of 10 : positive (label = 1)\nAt most 4 stars out of 10 : negative (label = 0)\n50/50 train/test split\nEvaluation accuracy\n\n## Features: bag of 1-grams with TF-IDF values:\nExtremely sparse feature matrix - close to 97% are zeros\n\n## Model: Logistic regression\n\np(y=1|x)=σ(w.Tx)\n\nLinear classification model\nCan handle sparse data\nFast to train\nWeights can be interpreted ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpratishtha-abrol%2Fsentimentanalysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpratishtha-abrol%2Fsentimentanalysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpratishtha-abrol%2Fsentimentanalysis/lists"}