NLP Sentiment Analysis Pipeline
Airline Sentiment AI
A natural language processing project that turns airline-related customer text into structured sentiment data so themes can be explored as a dataset rather than read message by message.
Challenge / Objective
Objective: classify sentiment in airline customer text and make the labelled output explorable alongside the rest of the data.
Approach
Build a conventional, well-understood NLP pipeline — clean, tokenise, extract features, classify — and keep each step reproducible so the analysis can be repeated on new data.
Solution
A preprocessing and classification pipeline producing a sentiment label per record, plus exploratory analysis over the labelled dataset.
- Text preprocessing
- Supervised sentiment classification
- Held-out evaluation
- Exploratory analysis of labelled data
Architecture
Text data → preprocessing → feature extraction → classification → analysis. Each stage is separable, so preprocessing choices can be changed without rebuilding the rest.
How it flows
- Text data
- Preprocessing
- Feature extraction
- Classification
- Analysis
Implementation
Python for cleaning, tokenisation and feature extraction; scikit-learn for supervised classification; Pandas for dataset handling and exploration. Evaluation uses a held-out split.
Technology
AI / ML
- NLP
- Machine Learning
- scikit-learn
Development
- Python
Data
- Pandas
Current state
Current state: the pipeline trains, classifies and evaluates on a held-out split. No accuracy figure is published here because none has been independently verified for publication.
Key takeaways
Preprocessing decisions — how text is normalised and which features are kept — moved results more than model selection, so that is where the effort was worth spending.
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Project
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