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Toni Chikere

Toni Chikere

Sentiment Annotation Specialist and Financial Data Analyst

Nigeria flagLagos, Nigeria
$20.00/hrEntry LevelOther

Key Skills

Software

Other

Top Subject Matter

Crypto Community Sentiment Analysis
Financial Markets and Trading Data
Behavioural Data Analysis

Top Data Types

ImageImage
AudioAudio
VideoVideo

Top Task Types

Emotion RecognitionEmotion Recognition
ClassificationClassification
Text GenerationText Generation
Data CollectionData Collection
SegmentationSegmentation
Object DetectionObject Detection
Question AnsweringQuestion Answering

Freelancer Overview

I come to data labelling from an unusual place and that unusual place is exactly what makes my annotations better. Three industries. The same core skill in each. As a hydrographic surveyor, I processed seabed data where a single classification error had real consequences. As a financial market analyst, I built frameworks for identifying patterns in human behaviour across live markets, under pressure, in real time, with no room for approximation. As a sentiment analyst, I developed personal annotation systems to distinguish organic community concern from coordinated manipulation, a distinction that requires contextual judgment no algorithm has yet replicated. The thread connecting all three is precision under complexity. I don’t label what something looks like. I label what it is, and I know the difference because I’ve spent years finding the logic beneath the surface in environments where being wrong was not an option. That’s what I bring to AI training data. Not just accurate labels, but the expert level reasoning behind them.

Entry LevelEnglish

Labeling Experience

Digital Sentiment Analyst

TextEmotion Recognition
Monitored, annotated, and classified real-time user sentiment from social media platforms during periods of market volatility. Developed a personal framework to categorize sentiment into defined labels such as positive, FUD, negative feedback, accusations, and threats. Compiled structured sentiment datasets and relayed actionable findings to supervisors for project decision-making. • Conducted real-time sentiment analysis across Telegram, Twitter/X, and Discord. • Annotated sentiment shifts and trigger events influencing community mood. • Collaborated with team members to ensure annotation consistency. • Utilized Google Sheets for data logging, organization, and visualization.

Monitored, annotated, and classified real-time user sentiment from social media platforms during periods of market volatility. Developed a personal framework to categorize sentiment into defined labels such as positive, FUD, negative feedback, accusations, and threats. Compiled structured sentiment datasets and relayed actionable findings to supervisors for project decision-making. • Conducted real-time sentiment analysis across Telegram, Twitter/X, and Discord. • Annotated sentiment shifts and trigger events influencing community mood. • Collaborated with team members to ensure annotation consistency. • Utilized Google Sheets for data logging, organization, and visualization.

2025 - Present

Community Management

TextData Collection
Performed sentiment analysis on community responses related to product updates and announcements. Designed and deployed structured community polls to systematically gather categorized feedback. Logged and relayed sentiment and annotated responses to supervisors, transforming qualitative feedback into structured insights. • Tracked and catalogued recurring feedback and community concerns. • Systematically grouped and analyzed responses by sentiment and engagement pattern. • Synthesized poll results and community feedback into structured supervisory reports. • Utilized digital platforms for data collection and annotation tasks.

Performed sentiment analysis on community responses related to product updates and announcements. Designed and deployed structured community polls to systematically gather categorized feedback. Logged and relayed sentiment and annotated responses to supervisors, transforming qualitative feedback into structured insights. • Tracked and catalogued recurring feedback and community concerns. • Systematically grouped and analyzed responses by sentiment and engagement pattern. • Synthesized poll results and community feedback into structured supervisory reports. • Utilized digital platforms for data collection and annotation tasks.

2021 - 2024

Education

N

Nnamdi Azikiwe University

Bachelor of Science, Surveying and Geoinformatics

Bachelor of Science
2012 - 2017

Work History

I

Independent

Financial Market Analyst/Trader

Lagos
2023 - Present
I

Innovion

Community Manager

N/A
2021 - 2024