Topic classification is a supervised machine learning technique, one that needs training before being able to automatically analyze texts. First, we'll delve into what topic modeling is, how it works, and how it compares to topic classification.
Work Flow Process :
- Data Understanding
- Data Exploration
- Feature Engineering with :
Symbol Remover Lemmatization Stopword remover Lowercasing
- Model Selection
- Model Evaluation
The Best Model is Using Linear Support Vector Machine with accuracy 87.9%
list of requirement package :
Kumparanian
nlp-id
pandas
numpy
matplotlib
sklearn
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Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
