{"id":24746539,"url":"https://github.com/singhvishal003/bert-sentiment","last_synced_at":"2026-04-09T21:01:42.353Z","repository":{"id":264294373,"uuid":"892634758","full_name":"Singhvishal003/BERT-Sentiment","owner":"Singhvishal003","description":"Sentiment Analysis Model To Detect the Sentiments.","archived":false,"fork":false,"pushed_at":"2024-11-23T07:21:56.000Z","size":3,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-28T04:31:20.307Z","etag":null,"topics":["beautifulsoup","numpy","pandas","requests","torch","torchvision"],"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/Singhvishal003.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":"2024-11-22T13:39:51.000Z","updated_at":"2024-11-26T04:58:28.000Z","dependencies_parsed_at":"2024-11-23T10:03:58.836Z","dependency_job_id":null,"html_url":"https://github.com/Singhvishal003/BERT-Sentiment","commit_stats":null,"previous_names":["singhvishal003/bert-sentiment"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Singhvishal003%2FBERT-Sentiment","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Singhvishal003%2FBERT-Sentiment/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Singhvishal003%2FBERT-Sentiment/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Singhvishal003%2FBERT-Sentiment/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Singhvishal003","download_url":"https://codeload.github.com/Singhvishal003/BERT-Sentiment/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245041865,"owners_count":20551474,"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":["beautifulsoup","numpy","pandas","requests","torch","torchvision"],"created_at":"2025-01-28T04:29:05.405Z","updated_at":"2026-04-09T21:01:42.299Z","avatar_url":"https://github.com/Singhvishal003.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Sentiment Analysis Model\n\n### Overview\nSentiment analysis is a natural language processing (NLP) technique used to determine the sentiment expressed in a piece of text. This model classifies text into positive, negative, or neutral categories.\n\n### Key Features\n- *Text Classification*: Analyzes text data to identify sentiment.\n- *Machine Learning*: Utilizes machine learning algorithms for accurate sentiment prediction.\n- *NLP Techniques*: Employs tokenization, stemming, and other NLP methods.\n\n### Applications\n- *Social Media Monitoring*: Analyzes tweets, posts, and comments to gauge public opinion.\n- *Customer Feedback*: Evaluates reviews and feedback to understand customer satisfaction.\n- *Market Research*: Assesses sentiment in news articles and reports for market analysis.\n\n### How It Works\n1. *Data Collection*: Gather text data from various sources.\n2. *Preprocessing*: Clean and prepare the data (e.g., removing stop words, tokenization).\n3. *Feature Extraction*: Convert text into numerical features using techniques like TF-IDF.\n4. *Model Training*: Train the model using labeled datasets.\n5. *Prediction*: Use the trained model to predict sentiment on new text data.\n\n### Requirements\n- Python 3.7+\n- Libraries: numpy, pandas, scikit-learn, nltk\n\n### Installation\nbash\npip install -r requirements.txt\n\n\n### Usage\npython\nfrom sentiment_model import SentimentAnalyzer\n\n# Initialize the analyzer\nanalyzer = SentimentAnalyzer()\n\n# Analyze sentiment\ntext = \"I love this product!\"\nsentiment = analyzer.predict(text)\nprint(f\"Sentiment: {sentiment}\")\n\n\n### Contributing\nContributions are welcome! 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