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 practice word-level analysis and understand how simple NLP techniques work.

Steps

• HTML tags, extra spaces, and punctuation are removed.

• The text is split into words and counted.

• Common stop words are filtered out.

• Sentiment is classified based on positive and negative word counts.

• Frequent words are used to suggest possible 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.