Data

Tools

Indexes

Applications

Experiments

Open Source Science

24001109-19-azi-challenge-platinum

API for sentiment classification using Keras NN, LSTM, and sklearn MLP Classifier

https://github.com/chocohaze/24001109-19-azi-challenge-platinum

Science Score: 31.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (0.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

API for sentiment classification using Keras NN, LSTM, and sklearn MLP Classifier

Basic Info
  • Host: GitHub
  • Owner: chocohaze
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 165 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Citation

README.md

24001109-19-azi-challenge-platinum

Owner

  • Name: Aziz Awaludin
  • Login: chocohaze
  • Kind: user

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.",
}

GitHub Events

Total
Last Year

Dependencies

requirements.txt pypi
  • Babel ==2.13.1
  • Flask ==3.0.0
  • Jinja2 ==3.1.2
  • Markdown ==3.6
  • MarkupSafe ==2.1.3
  • Pillow ==10.1.0
  • PySastrawi ==1.2.0
  • PyYAML ==6.0.1
  • Pygments ==2.16.1
  • QtPy ==2.4.1
  • Send2Trash ==1.8.2
  • Werkzeug ==3.0.1
  • absl-py ==2.1.0
  • aiofiles ==23.2.1
  • aiohttp ==3.8.6
  • aiosignal ==1.3.1
  • altair ==5.1.2
  • annotated-types ==0.6.0
  • antiorm ==1.2.1
  • anyio ==3.7.1
  • argon2-cffi ==23.1.0
  • argon2-cffi-bindings ==21.2.0
  • arrow ==1.3.0
  • asttokens ==2.4.1
  • astunparse ==1.6.3
  • async-lru ==2.0.4
  • async-timeout ==4.0.3
  • attrs ==23.1.0
  • backcall ==0.2.0
  • beautifulsoup4 ==4.12.2
  • bleach ==6.1.0
  • blinker ==1.6.3
  • cachetools ==5.3.3
  • certifi ==2023.7.22
  • cffi ==1.16.0
  • charset-normalizer ==3.3.1
  • click ==8.1.7
  • colorama ==0.4.6
  • comm ==0.1.4
  • contourpy ==1.1.1
  • cycler ==0.12.1
  • dacktool ==0.0.7
  • db ==0.1.1
  • db-sqlite3 ==0.0.1
  • dbstream ==0.1.21
  • debugpy ==1.8.0
  • decorator ==5.1.1
  • defusedxml ==0.7.1
  • dm-tree ==0.1.8
  • et-xmlfile ==1.1.0
  • executing ==2.0.0
  • fastapi ==0.104.1
  • fastjsonschema ==2.18.1
  • ffmpy ==0.3.1
  • filelock ==3.13.1
  • flasgger ==0.9.7.1
  • flatbuffers ==24.3.25
  • fonttools ==4.43.1
  • fqdn ==1.5.1
  • frozenlist ==1.4.0
  • fsspec ==2023.10.0
  • gast ==0.4.0
  • google-api-core ==2.17.1
  • google-auth ==2.28.2
  • google-auth-oauthlib ==1.0.0
  • google-pasta ==0.2.0
  • googleapis-common-protos ==1.63.0
  • gradio ==3.40.1
  • gradio_client ==0.7.0
  • grpcio ==1.63.0
  • h11 ==0.14.0
  • h5py ==3.11.0
  • httpcore ==0.18.0
  • httpx ==0.25.0
  • huggingface-hub ==0.18.0
  • idna ==3.4
  • importlib-resources ==6.1.0
  • ipykernel ==6.26.0
  • ipython ==8.16.1
  • ipython-genutils ==0.2.0
  • ipywidgets ==8.1.1
  • isoduration ==20.11.0
  • itsdangerous ==2.1.2
  • jax ==0.4.28
  • jedi ==0.19.1
  • joblib ==1.3.2
  • json5 ==0.9.14
  • jsonpointer ==2.4
  • jsonschema ==4.19.1
  • jsonschema-specifications ==2023.7.1
  • jupyter ==1.0.0
  • jupyter-console ==6.6.3
  • jupyter-events ==0.8.0
  • jupyter-lsp ==2.2.0
  • jupyter_client ==8.5.0
  • jupyter_core ==5.4.0
  • jupyter_server ==2.9.1
  • jupyter_server_terminals ==0.4.4
  • jupyterlab ==4.0.7
  • jupyterlab-pygments ==0.2.2
  • jupyterlab-widgets ==3.0.9
  • jupyterlab_server ==2.25.0
  • keras ==3.3.3
  • kiwisolver ==1.4.5
  • libclang ==18.1.1
  • linkify-it-py ==2.0.2
  • markdown-it-py ==2.2.0
  • matplotlib ==3.8.0
  • matplotlib-inline ==0.1.6
  • mdit-py-plugins ==0.3.3
  • mdurl ==0.1.2
  • mistune ==3.0.2
  • ml-dtypes ==0.3.2
  • multidict ==6.0.4
  • namex ==0.0.8
  • nbclient ==0.8.0
  • nbconvert ==7.9.2
  • nbformat ==5.9.2
  • nest-asyncio ==1.5.8
  • nltk ==3.8.1
  • notebook ==7.0.6
  • notebook_shim ==0.2.3
  • numpy ==1.23.5
  • oauthlib ==3.2.2
  • openpyxl ==3.1.2
  • opt-einsum ==3.3.0
  • optree ==0.11.0
  • orjson ==3.9.10
  • overrides ==7.4.0
  • packaging ==23.2
  • pandas ==2.1.2
  • pandocfilters ==1.5.0
  • parso ==0.8.3
  • pickleshare ==0.7.5
  • platformdirs ==3.11.0
  • prometheus-client ==0.17.1
  • prompt-toolkit ==3.0.39
  • protobuf ==4.25.3
  • psutil ==5.9.6
  • pure-eval ==0.2.2
  • pyarrow ==15.0.0
  • pyasn1 ==0.5.1
  • pyasn1-modules ==0.3.0
  • pycparser ==2.21
  • pydantic ==2.4.2
  • pydantic_core ==2.10.1
  • pydub ==0.25.1
  • pylance ==0.10.4
  • pyparsing ==3.1.1
  • pyproject ==1.3.1
  • python-dateutil ==2.8.2
  • python-json-logger ==2.0.7
  • python-multipart ==0.0.6
  • pytz ==2023.3.post1
  • pywin32 ==306
  • pywinpty ==2.0.12
  • pyzmq ==25.1.1
  • qtconsole ==5.4.4
  • referencing ==0.30.2
  • regex ==2023.10.3
  • requests ==2.31.0
  • requests-oauthlib ==2.0.0
  • rfc3339-validator ==0.1.4
  • rfc3986-validator ==0.1.1
  • rich ==13.6.0
  • rpds-py ==0.10.6
  • rsa ==4.9
  • scikit-learn ==1.3.2
  • scipy ==1.11.4
  • seaborn ==0.13.0
  • semantic-version ==2.10.0
  • shellingham ==1.5.4
  • six ==1.16.0
  • sklearn ==0.0.post11
  • slite ==0.0.2
  • sniffio ==1.3.0
  • soupsieve ==2.5
  • stack-data ==0.6.3
  • starlette ==0.27.0
  • tensorboard ==2.16.2
  • tensorboard-data-server ==0.7.2
  • tensorflow ==2.16.1
  • tensorflow-estimator ==2.12.0
  • tensorflow-intel ==2.16.1
  • tensorflow-io-gcs-filesystem ==0.31.0
  • termcolor ==2.4.0
  • terminado ==0.17.1
  • threadpoolctl ==3.2.0
  • tinycss2 ==1.2.1
  • tomlkit ==0.12.0
  • toolz ==0.12.0
  • tornado ==6.3.3
  • tqdm ==4.66.1
  • traitlets ==5.12.0
  • typer ==0.9.0
  • types-python-dateutil ==2.8.19.14
  • typing_extensions ==4.5.0
  • tzdata ==2023.3
  • uc-micro-py ==1.0.2
  • uri-template ==1.3.0
  • urllib3 ==2.0.7
  • uvicorn ==0.23.2
  • wcwidth ==0.2.8
  • webcolors ==1.13
  • webencodings ==0.5.1
  • websocket-client ==1.6.4
  • websockets ==11.0.3
  • widgetsnbextension ==4.0.9
  • wordcloud ==1.9.2
  • wrapt ==1.14.1
  • yarl ==1.9.2