Moronke T.

Key Skills

Software

Other

Top Subject Matter

Sentiment analysis and text classification

Spam detection and message classification

AI response evaluation and quality assessment

Top Data Types

Text

Top Task Types

Classification

Entity (NER) Classification

Freelancer Overview

Data Annotation Portfolio - Sentiment classification. Core strengths include Other and ChatGPT. Education includes Bachelor of Business Administration, N/A. AI-training focus includes data types such as Text and labeling workflows including Classification, Evaluation, and Rating.

Labeling Experience

Named Entity Recognition (NER)

OtherTextEntity (NER) Classification

Annotated text for Named Entity Recognition by identifying people, organizations, locations, and dates using predefined labeling guidelines. Extracted structured information from unstructured text while maintaining accuracy and consistency. Performed verification to ensure extracted entities matched the intended categories. • Labeled entities by type (people, orgs, locations, dates) • Followed NER labeling instructions • Verified extracted spans and categories • Organized labeled entity outputs

Not specified

AI Response Evaluation

Text

Compared AI-generated responses against quality guidelines to assess accuracy, clarity, completeness, and relevance. Selected preferred responses using objective reasoning and documented evaluation decisions. Used these evaluations to support higher-quality model outputs. • Rated responses per quality rubric • Verified factual consistency and coherence • Compared multiple candidate answers • Recorded and justified evaluation outcomes

Not specified

Spam Detection & Text Classification

OtherTextClassification

Labeled email and text messages as Spam or Not Spam using predefined labeling criteria. Ensured consistent distinction between legitimate messages and promotional, phishing, and fraudulent content. Maintained annotation standards to improve reliability and accuracy. • Applied spam-label decision rules • Ensured consistency across samples • Annotated messages for fraud/phishing indicators • Produced structured labeled outputs for evaluation

Not specified

Data Annotation Portfolio - Sentiment classification

OtherTextClassification

Completed sentiment classification for text using predefined Positive, Negative, and Neutral labels while following annotation guidelines. Performed quality checks by reviewing outputs for grammar, coherence, factual consistency, and overall quality. Organized labeled text data to support accurate downstream use. • Applied consistent sentiment labeling standards • Reviewed and corrected classification outputs • Evaluated AI-generated text against annotation criteria • Organized and prepared classified data for use

Not specified

Education

Work History