{"id":23215555,"url":"https://github.com/dscmatter/tf-idf-document_scorer","last_synced_at":"2025-04-05T13:42:56.886Z","repository":{"id":219741766,"uuid":"749788930","full_name":"DSCmatter/TF-IDF-Document_Scorer","owner":"DSCmatter","description":"TF-IDF (Term frequency, Inverse Document Frequency) is an algorithm or way to score the importance of words (or 'terms') based on how frequently they appear","archived":false,"fork":false,"pushed_at":"2024-06-28T09:52:24.000Z","size":24,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-11T09:51:13.253Z","etag":null,"topics":["algorithm","python","tf-idf-score"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DSCmatter.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-01-29T12:00:18.000Z","updated_at":"2024-06-28T09:52:27.000Z","dependencies_parsed_at":"2024-01-29T14:43:32.278Z","dependency_job_id":"2f9adfdc-6403-469c-b9b4-98fd25a5e900","html_url":"https://github.com/DSCmatter/TF-IDF-Document_Scorer","commit_stats":null,"previous_names":["vasantmogia/tf-idf--document-scorer-","dscmatter/tf-idf-document_scorer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DSCmatter%2FTF-IDF-Document_Scorer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DSCmatter%2FTF-IDF-Document_Scorer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DSCmatter%2FTF-IDF-Document_Scorer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DSCmatter%2FTF-IDF-Document_Scorer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DSCmatter","download_url":"https://codeload.github.com/DSCmatter/TF-IDF-Document_Scorer/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247345790,"owners_count":20924098,"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":["algorithm","python","tf-idf-score"],"created_at":"2024-12-18T20:17:17.396Z","updated_at":"2025-04-05T13:42:56.818Z","avatar_url":"https://github.com/DSCmatter.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TF-IDF\nTF-IDF (Term frequency, Inverse Document Frequency) is an algorithm or way to score the importance of words (or 'terms') based on how frequently they appear\n\n which means\n - If a word appears frequently in a document, it's important. Give the word a high score.\n - But if a word appears in many documents, it's not a unique identifier. Give the word a low score.\n\n## Prerequisites:\nBefore using this TF-IDF implementation, ensure you have the following packages installed:\n\n- textblob\n- nltk\n\nYou can install these packages using pip:\n'pip install textblob nltk'\n\n## Improved TF-IDF Implementation\nThis implementation of TF-IDF features improvements such as:\n\n- Utilizing NLTK to download stopwords and tokenize the text.\n- Filtering out stopwords from the document before calculating TF-IDF scores.\n- Lowercasing the words to ensure case insensitivity.\n- Calculating TF-IDF scores based on the filtered document.\n\n## Usage\n- Ensure you have Python installed on your system.\n- Install the required packages using pip as mentioned in the Prerequisites section.\n- Clone or download this repository.\n- Navigate to the directory containing the TF-IDF script.\n- Run the script and follow the prompts to enter the location of the document file.\n\nThe script will calculate the TF-IDF scores and display the top words along with their scores.\n\n## Examples\nTwo example text files have been provided in the repository for testing the TF-IDF algorithm.\n\n- text.txt\n- text2.txt\n  \n## Further Reading\n- For more information on TF-IDF and its applications, visit the following link:\n\n- [TF-IDF Explained - Steven Loria](https://stevenloria.com/tf-idf/)\n\n## License\n- This project is licensed under the [MIT License](LICENSE) - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdscmatter%2Ftf-idf-document_scorer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdscmatter%2Ftf-idf-document_scorer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdscmatter%2Ftf-idf-document_scorer/lists"}