British Airways

This code reads the scraped reviews from the "reviews.csv" file and vectorizes the text data using the CountVectorizer from scikit-learn. The vectorized data is then fitted to an LDA model with 5 topics using the LatentDirichletAllocation function.

The code then prints the most important words for each topic, which are the words that are most strongly associated with that topic based on their frequency in the reviews. You can adjust the number of topics (n_topics) to explore different aspects of the review data.

Once you have identified the most important words for each topic, you can analyze the topics to gain insights into what customers are saying about the airline. For example, if one of the topics is related to "food" and includes words such as "meal", "menu", and "quality", you could infer that customers have strong opinions about the food served on the airline.