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.

We created a bar chart to explore the distribution of sentiments in the dataset, which was displayed for visualization.

The key words in positive comments include 'okay', 'best', 'good', and 'awesome' among others. For negative comments, common terms are 'happy', 'bull', and 'shit'. These keywords can help identify the tone of customer feedback in the dataset.

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.

The user requested a visualization of the number of comments per cluster. A bar chart was created and displayed, showing the distribution of comments across different clusters.