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Topic Modeling: Switching from implementation to XGBoost #37

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ghost opened this issue Sep 29, 2020 · 0 comments
Open

Topic Modeling: Switching from implementation to XGBoost #37

ghost opened this issue Sep 29, 2020 · 0 comments
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enhancement New feature or request hacktoberfest

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@ghost
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ghost commented Sep 29, 2020

Feature description:

Change the implementation of the current topic modeling to xgboost, since it obtained better results in new studies.

Research: https://drive.google.com/file/d/11CdWkqLgQ3f-1FNI1AvNwKEIkYO336Z_/view?usp=sharing and https://drive.google.com/file/d/16ifjhucCLsFbN3Y2PgZWfORj2-viepmg/view?usp=sharing

Need:
There was a great improvement in the model. Accuracy: 83%

Implementation:

  • The texts were labeled with one of the chosen categories.
  • Then, a study was carried out in the database, to remove the categories that appear in less than 90 texts.
  • And then it was trained using XGBoost and BI-LSTM, and XGBoost achieved superior accuracy.
@ghost ghost added the enhancement New feature or request label Sep 29, 2020
@ghost ghost self-assigned this Sep 29, 2020
@ghost ghost changed the title TopicModeling: Trocar a implementação pelo XGBoost Topic Modeling: Switching from implementation to XGBoost Sep 29, 2020
@ghost ghost mentioned this issue Sep 29, 2020
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@ghost ghost mentioned this issue Nov 6, 2020
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@ghost ghost added the hacktoberfest label Oct 1, 2021
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