To analyze the comments and understand the customers' intent, we'll perform a sentiment analysis. This process will help us categorize the comments into positive, negative, or neutral sentiments. Let's proceed with this analysis to better understand the customer feedback.

We have analyzed customer comments from the dataset to determine the intent behind them through sentiment analysis. The sentiments were categorized as Positive, Neutral, or Negative based on the polarity of each comment. Most comments in the sample analyzed are Positive.

Attempted to visualize the top positive and negative words from customer comments, but encountered an error due to missing imports. The code was corrected and successfully executed, displaying a bar graph comparing the word frequencies by sentiment.

We've loaded the dataset and displayed its structure, which includes various customer attributes like tenure, services used, and churn status. Additionally, we have filtered comments from churned customers and applied TF-IDF vectorization followed by KMeans clustering to categorize these comments into three clusters.