Data Annotation and Quality Assurance

Project Overview

This project demonstrates a structured workflow for text annotation and quality assurance, using a sample sentiment-classification dataset.

It showcases skills relevant to AI data training, data annotation, dataset quality review, and LLM evaluation support.

Project Objectives

• Apply consistent sentiment labels using documented guidelines.
• Provide clear reasons for annotation decisions.
• Identify mixed sentiment and potentially ambiguous examples.
• Check annotation completeness and consistency.
• Document dataset limitations and opportunities for improvement.

Dataset

The sample dataset contains 10 text records covering four sentiment categories:

• Positive: expresses satisfaction or approval.
• Negative: expresses dissatisfaction or criticism.
• Neutral: provides information without clear positive or negative sentiment.
• Mixed: contains meaningful positive and negative sentiment.

Each record includes a unique ID, text, sentiment label, and explanation.

Project Files

File	Purpose
annotation_samples.csv	Sample annotated text records
annotation_guidelines.md	Label definitions and decision rules
qa_report.md	Manual quality assurance findings
limitations.md	Dataset limitations and recommended improvements

Quality Assurance Approach

The sample workflow includes checks for:

• Unique record identifiers.
• Required fields and label validity.
• Consistency between text, labels, and explanations.
• Appropriate handling of mixed sentiment.
• Ambiguous examples that may need further review.

Skills Demonstrated

• Text data annotation
• Sentiment classification
• Guideline-based decision-making
• Annotation consistency checks
• Error and ambiguity identification
• Quality assurance documentation
• Structured CSV data handling

Limitations

This is a small demonstration dataset created for portfolio purposes. It does not represent a production client dataset, and it does not establish statistical accuracy or inter-annotator agreement.

Future Improvements

• Expand the dataset with more varied examples.
• Add challenging cases involving sarcasm and context.
• Compare labels from independent annotators.
• Track disagreement patterns and improve the guidelines.

Intended Application

This project demonstrates foundational skills relevant to AI training, data annotation, dataset quality assurance, and LLM evaluation support.

The examples are educational demonstrations rather than claims of performance on a real client's dataset.