This lab analyses a given text corpus by cleaning it, removing stop words, and calculating insights like word frequency, sentiment, and possible topics.

Purpose

To help you with word analysis.

Steps

• HTML tags, extra spaces, and punctuation are removed, and the text is split into words for a word count.

• Non-informative words are filtered out, and sentiment is classified based on positive/negative word counts, while frequent words suggest potential topics.

Behind the Process

​Topics are generated by clustering related words, and sentiment is classified based on the count of positive and negative words.

Note

We omitted common stop words (e.g., "the," "is") that don't contribute to meaningful analysis.