Airline Sentiment AI

Overview

A natural language processing project that analyses sentiment in airline-related customer text. Free-text feedback is preprocessed and classified so recurring themes can be explored as structured data rather than read one message at a time.

Problem / Objective

Objective: classify sentiment in airline customer text data and make the results explorable as a dataset.

Free-text feedback is unstructured, so patterns and negative trends are hard to identify without reading everything manually.

Solution

A text preprocessing and classification pipeline that labels sentiment and produces an aggregable output for exploration.

Key capabilities

• Text preprocessing
• Sentiment classification
• NLP feature extraction
• Exploratory data analysis

Technology

• AI / ML

  • NLP
  • Machine Learning
  • scikit-learn

• Development

  • Python

• Data

  • Pandas

Implementation

Python handles cleaning, tokenisation and feature extraction. A supervised classifier is trained with scikit-learn and evaluated on a held-out split, with Pandas used throughout for dataset handling and exploration.

Related services

• Python, Data & AI