Gist e049bb6d7696d1b2d4ff242e68fcd062

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Gist e049bb6d7696d1b2d4ff242e68fcd062

✓ Published0🌍 Public

Last edited Dec 29, 2021

Created on Dec 29, 2021

This example analyzes text data by performing topic modeling and visualization. It shows the resulting topics and their associated terms, revealing latent themes in the dataset. The code uses NLTK for tokenization and stopword removal, TextBlob for sentiment analysis, and Gensim’s LdaModel and CoherenceModel for topic extraction. Plotly’s `scatter` and `bar` traces render the interactive visualizations, with data processed from a pandas DataFrame. The rendering approach relies on Plotly’s offline mode, initiated via `pyo.init_notebook_mode()`.

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