This example shows a browser-based text analytics pipeline that scrapes and processes web content before visualizing topic models. The code first uses Selenium with Chrome WebDriver to scrape data, then applies NLP libraries including NLTK, spaCy, TextBlob, and gensim’s LdaModel for topic modeling and coherence scoring. The final visualization employs Plotly’s `plotly.express` and `plotly.graph_objs` to render interactive charts, with `pyo.init_notebook_mode()` enabling notebook display. The rendering approach is evident from the use of `go` traces and `px` figures, though the specific chart type is not shown in the code snippet. The data source is dynamic web pages, as indicated by the Selenium scraping logic.