{"id":29556481,"url":"https://github.com/theveryhim/massive-text-processing","last_synced_at":"2025-07-18T10:11:47.283Z","repository":{"id":302610444,"uuid":"1013014222","full_name":"theveryhim/Massive-text-processing","owner":"theveryhim","description":"cleaning, processing and analysis of papers' dataset in pyspark(rdd) framework","archived":false,"fork":false,"pushed_at":"2025-07-04T22:24:14.000Z","size":1376,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-04T23:27:35.022Z","etag":null,"topics":["big-data","data-analysis","frequent-itemsets","massive-datasets","pyspark","text-preprocessing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/theveryhim.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,"zenodo":null}},"created_at":"2025-07-03T08:23:54.000Z","updated_at":"2025-07-04T22:24:17.000Z","dependencies_parsed_at":"2025-07-04T23:37:42.557Z","dependency_job_id":null,"html_url":"https://github.com/theveryhim/Massive-text-processing","commit_stats":null,"previous_names":["theveryhim/massive-text-processing-1","theveryhim/massive-text-processing"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/theveryhim/Massive-text-processing","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FMassive-text-processing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FMassive-text-processing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FMassive-text-processing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FMassive-text-processing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/theveryhim","download_url":"https://codeload.github.com/theveryhim/Massive-text-processing/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FMassive-text-processing/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265738681,"owners_count":23820167,"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":["big-data","data-analysis","frequent-itemsets","massive-datasets","pyspark","text-preprocessing"],"created_at":"2025-07-18T10:11:46.600Z","updated_at":"2025-07-18T10:11:47.272Z","avatar_url":"https://github.com/theveryhim.png","language":"Jupyter Notebook","readme":"# Large-scale data analysis in pyspark framework\n\nSome of tasks done in this Repo([papers' dataset](https://drive.google.com/file/d/1-EhpZaY5gvbgNuEU5IskmlQ0EnNAG5cu/view?usp=drive_link)):\n- Clean the texts in the title and abstract fields if needed. \n- Remove mathematical symbols, meaningless characters in the text, remove stopwords, etc. \n- Calculate the number of articles in each category (e.g., ph-hep or co.math).\n- Identify the category that has the most articles.\n- Analyze the distribution of the number of authors in each article. \n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"1.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n- Filter articles that have more than three authors and list their titles and authors. \n- Draw the number of articles registered in each year. \n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"2.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n- Extract and display 20 frequently used words in the abstract section of the article.\n```markdown\n5 most frequent words in abstract:\nmodel : 1188676\ndata : 917131\nresults : 859049\nshow : 831879\nusing : 809828\n```\n- Find the articles in which the word algorithm is mentioned in their abstract. \n- Count the number of words in the abstract of this article \n- Arrange them in descending order based on the number of words. \n- Display the five articles with the highest number of words in the abstract as the final result.   \n```markdown\nTop 5 articles with the highest word counts in their abstract (containing 'algorithm'):\nTitle: The Nonlinearity Coefficient - A Practical Guide to Neural Architecture\n  Design, Word Count: 498\nTitle: Generating a Generic Fluent API in Java, Word Count: 488\nTitle: Boxicity and Poset Dimension, Word Count: 484\nTitle: An Anytime Algorithm for Optimal Coalition Structure Generation, Word Count: 484\nTitle: McMini: A Programmable DPOR-Based Model Checker for Multithreaded\n  Programs, Word Count: 475\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheveryhim%2Fmassive-text-processing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheveryhim%2Fmassive-text-processing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheveryhim%2Fmassive-text-processing/lists"}