api-for-data-cleansing-and-connect-to-database-management-using-sqlite

Config files for my GitHub profile.

https://github.com/jongskuy/api-for-data-cleansing-and-connect-to-database-management-using-sqlite

Science Score: 18.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
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.zenodo.json file
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DOI references
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Academic email domains
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (2.4%) to scientific vocabulary

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config github-config

Last synced: 4 months ago · JSON representation ·

Repository

Config files for my GitHub profile.

Basic Info

Host: GitHub
Owner: Jongskuy
Language: Python
Default Branch: main
Homepage: https://github.com/Jongskuy
Size: 2.03 MB

Statistics

Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Topics

config github-config

Created about 3 years ago · Last pushed about 2 years ago

Metadata Files

Readme Citation

README.md

👋 Hi, I’m @Tanupitra
👀 I’m interested in Data Science
🌱 I’m currently learning Python
📫 Reach me at https://www.linkedin.com/in/tanupitra/

Owner

Name: Tanu Pitra
Login: Jongskuy
Kind: user
Location: Indonesia

Repositories: 1
Profile: https://github.com/Jongskuy

👋 Hi, I’m @Tanupitra - 👀 I’m interested in Data Science - 🌱 I’m currently learning Python - 📫 Reach me at https://www.linkedin.com/in/tanupitra/

Citation (citation.bib)

@inproceedings{ibrohim-budi-2019-multi,
    title = "Multi-label Hate Speech and Abusive Language Detection in {I}ndonesian Twitter",
    author = "Ibrohim, Muhammad Okky  and
      Budi, Indra",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3506",
    doi = "10.18653/v1/W19-3506",
    pages = "46--57",
    abstract = "Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.",
}

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