{"id":28284593,"url":"https://github.com/thevarunsharma/text-sentiment-classification","last_synced_at":"2026-05-07T02:33:45.572Z","repository":{"id":111637788,"uuid":"152280672","full_name":"thevarunsharma/Text-Sentiment-Classification","owner":"thevarunsharma","description":"A web application with Python backend which predicts the sentiment/mood (positive or negative) associated with input text. ","archived":false,"fork":false,"pushed_at":"2019-02-04T09:22:33.000Z","size":422,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-07-04T20:41:20.804Z","etag":null,"topics":["deep-learning","flask-application","keras","lstm-neural-networks","machine-learning","nlp","python","sentiment-analysis","tensorflow"],"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/thevarunsharma.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,"zenodo":null}},"created_at":"2018-10-09T16:01:50.000Z","updated_at":"2021-11-17T15:00:46.000Z","dependencies_parsed_at":"2023-05-24T18:00:14.865Z","dependency_job_id":null,"html_url":"https://github.com/thevarunsharma/Text-Sentiment-Classification","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/thevarunsharma/Text-Sentiment-Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thevarunsharma%2FText-Sentiment-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thevarunsharma%2FText-Sentiment-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thevarunsharma%2FText-Sentiment-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thevarunsharma%2FText-Sentiment-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thevarunsharma","download_url":"https://codeload.github.com/thevarunsharma/Text-Sentiment-Classification/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thevarunsharma%2FText-Sentiment-Classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32720191,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-07T02:14:30.463Z","status":"ssl_error","status_checked_at":"2026-05-07T02:14:29.405Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: 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":["deep-learning","flask-application","keras","lstm-neural-networks","machine-learning","nlp","python","sentiment-analysis","tensorflow"],"created_at":"2025-05-21T17:14:34.999Z","updated_at":"2026-05-07T02:33:45.568Z","avatar_url":"https://github.com/thevarunsharma.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text-Sentiment-Classification\nA model for sentiment classification on text (positive or negative), trained and built using keras which uses a RNN with LSTM units. Dataset used was taken from Kaggle: Amazon Reviews for Sentiment Analysis dataset (https://www.kaggle.com/bittlingmayer/amazonreviews), which included 3.6M training examples(2.88M train and 0.72M validation) and 400k test examples. The model achieved test accuracy of 93.6% and training accuracy of 94.6%.\n\nBased on this model created a web-user interface using Flask(Python) for backend, which takes as input a piece of text and returns the corresponding sentiment (positive/negative).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthevarunsharma%2Ftext-sentiment-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthevarunsharma%2Ftext-sentiment-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthevarunsharma%2Ftext-sentiment-classification/lists"}