In this project, I used natural language processing techniques to analyze tweets related to a leading airline company. The objective of the project was to understand the sentiment of customers towards the company and identify key issues that needed to be addressed.
- Collected a large dataset of tweets related to the airline company using the Twitter API
- Preprocessed the tweets using techniques such as lowercasing, removing stop words, and stemming
- Conducted sentiment analysis using NLP techniques to classify tweets into positive, negative, and neutral categories
- Visualized the results using bar graphs and word clouds to identify trends and patterns
- Performed topic modeling using LDA to identify common topics discussed in the tweets
- Conducted further analysis to understand the relationship between sentiment and topic
- The majority of tweets had a neutral sentiment, with a smaller proportion of negative and positive tweets
- The most common topics discussed in the tweets included flight cancellations, customer service, and baggage issues
- A strong relationship was found between negative sentiment and tweets related to flight cancellations and customer service issues
The expected results I achieved were an accurate model that can predict the sentiment of tweets from airline companies. I evaluated the model's accuracy and deployed it for making predictions on new tweets.
In conclusion, I successfully completed a project that analyzes the sentiment of tweets from airline companies using NLP and machine learning. The results of this project can be useful for airline companies who are interested in understanding their customers' opinions and perceptions. The model I developed can help them gain insights into customer sentiment and improve their services accordingly.