Web Scraping to get company Insights
Aug 28, 2023
Project Title: Web Scraping to get company Insights
Tools and Libraries used: Python, jupyter notebook, pandas,nltk, scikit-learn, textblob, matplotlib, wordcloud, Tableau
GitHub Repository: The repository link for the code and files - Link.
Approach:
Web Scraped 3621, British Airways reviews from Airline Quality website.
Preprocessed and cleaned text reviews.
Conducted topic modeling to uncover underlying themes.
Performed sentiment analysis to gauge reviews' sentiment.
Identified top positive and negative words.
Extracted common words across different topics.
Visualized sentiment distribution and word clouds.
Results:
Folder for all .csv result files - Link.
From the reviews 70.76% were positive, 28.61% negative and 0.63% neutral.
The word cloud common words across topics - service, time, food, seat and crew.
Positive reviews topics - smooth operations, attentive staff, comfortable seating and quality lounges.
Negative review topics - customer service issues, uncomfortable seats and challenges with boarding and communication.
Visualizations:
Sentiment distribution plot:
WordCloud:
Conclusion:
The analysis provided valuable insights into British Airways reviews, revealing prominent topics and sentiments.
Key positive and negative terms shed light on passengers' preferences and concerns.
Common words across topics pointed towards recurrent themes in reviews.