Sentiment Analysis of Airplane Reviews
This project performs sentiment analysis on airplane reviews using string vectorization and clustering techniques. It aims to uncover patterns and insights from customer feedback to understand the sentiments associated with different aspects of air travel
Air travel is an essential part of modern life, and customer reviews provide valuable insights into passenger experiences. This project utilizes natural language processing (NLP) techniques to analyze a dataset of airplane reviews, focusing on sentiment analysis, string vectorization, and clustering.
We use sentiment analysis to classify reviews as positive, negative, or neutral. Understanding passenger sentiment is crucial for airlines to improve their services. String Vectorization: Text data is converted into numerical form using string vectorization techniques, such as TF-IDF (Term Frequency-Inverse Document Frequency). Clustering:We apply clustering algorithms, i.e K-means, to group similar reviews together. This helps identify common themes or issues in the feedback.
Dataset: We use a dataset of airplane reviews, which includes text reviews along with sentiment labels. Dataset Link:https://www.kaggle.com/datasets/pyrotech/british-airways-reviews-unfiltered
Data Preprocessing:** Data preprocessing steps include text cleaning, tokenization, and feature extraction to prepare the text data for analysis.
- Clone the repository to your local machine.
- Install the required dependencies[python IDE,jupyter notebook]
- Run the Jupyter Notebook or Python script to execute the analysis.
- Customize the clustering algorithm, vectorization method, or dataset as needed.
- The project's results and insights gained from clustering reviews based on sentiment and content will be seen in code..
Contributions are welcome! If you have ideas for improvements or find any issues, please open an issue or submit a pull request.