{"id":25097704,"url":"https://github.com/jparkerweb/extract-topics","last_synced_at":"2025-07-23T23:04:50.085Z","repository":{"id":268340993,"uuid":"904035693","full_name":"jparkerweb/extract-topics","owner":"jparkerweb","description":"👽 Extract Topics ⇢ use LDA (Latent Dirichlet Allocation) to extract topics from text","archived":false,"fork":false,"pushed_at":"2025-01-25T05:56:17.000Z","size":174,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-13T23:47:48.170Z","etag":null,"topics":["extraction","lda","nlp","text","topic","topic-extraction"],"latest_commit_sha":null,"homepage":"https://www.npmjs.com/package/extract-topics","language":"JavaScript","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/jparkerweb.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","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":"2024-12-16T06:06:58.000Z","updated_at":"2025-01-25T05:54:04.000Z","dependencies_parsed_at":null,"dependency_job_id":"9e945a7c-7552-4c4d-8587-3fb5be8e7d12","html_url":"https://github.com/jparkerweb/extract-topics","commit_stats":null,"previous_names":["jparkerweb/lda-test","jparkerweb/topic-extraction"],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jparkerweb%2Fextract-topics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jparkerweb%2Fextract-topics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jparkerweb%2Fextract-topics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jparkerweb%2Fextract-topics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jparkerweb","download_url":"https://codeload.github.com/jparkerweb/extract-topics/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246746877,"owners_count":20827061,"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":["extraction","lda","nlp","text","topic","topic-extraction"],"created_at":"2025-02-07T17:34:19.924Z","updated_at":"2025-04-02T02:42:51.535Z","avatar_url":"https://github.com/jparkerweb.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 👽 Extract Topics\nUse LDA (Latent Dirichlet Allocation) to extract topics from text\n\nSimple NPM package for using Latent Dirichlet Allocation (LDA) for topic modeling on text inputs.\n\n![extract-topics](extractTopics.jpg)\n\n## Install\n\nInstall dependencies:\n\n```bash\nnpm install extractTopics\n```\n\n## Usage\n\n```bash\nimport { extractTopics } from 'extractTopics';\n\nconst result = await extractTopics(text, { numTopics, numTerms });\n\nconsole.log(result);\n```\n\n## API\n\n### topicExtraction(text, options)\n\nExtracts topics from input text using LDA.\n\n#### Parameters\n\n- `text` (string): The input text to analyze\n- `options` (object):\n  - `numTopics` (number, optional): Number of topics to extract. Default: 2\n  - `numTerms` (number, optional): Number of terms per topic. Default: 5\n\n#### Returns\n\nReturns a Promise that resolves to the LDA analysis result.\n\n### Example script\n\n```bash\nnpm run example\n```\n\nThe example will:\n1. Load sample text documents\n2. Apply LDA to extract the main topics\n3. Output the discovered topics and their key terms\n\n## About LDA\n\nLDA is an unsupervised learning method that discovers topics in text documents. It views documents as random mixtures over latent topics, where each topic is characterized by a distribution over words.\n\n---\n\n#### Project reference\n- https://www.npmjs.com/package/ldawithmorelanguages\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjparkerweb%2Fextract-topics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjparkerweb%2Fextract-topics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjparkerweb%2Fextract-topics/lists"}