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Quantify how people feel, with context,

clarity and precision

Used as a core data label, sentiment becomes a critical building block for structuring your data. Instead of guessing what people think, you measure and track how they feel, reliably and at scale.

Key benefits of Sentiment labelling

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Quantify qualitative data

One value to rate overall performance & measure general tone

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Filter out the noise

See through the neutrality & identify the important positive or negative conversation

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Real-time opinion

 Net Sentiment surfaces live insights that keep your teams informed and ready to respond

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Measure at scale

Save time through a measurement that sifts through thousands of data points

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Enrich training data

Use high-accuracy sentiment labels to train and validate internal models

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Enable automated prioritisation

Power CX workflows and triage systems by combining sentiment with other labels

Anatomy of Sentiment labelling

Each mention is processed by several layers of deep technical knowledge and the application of high-powered, intelligent AI and Human filters, ensuring structured, high quality data outputs.

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Why conventional sentiment labelling fails

Most off-the-shelf sentiment tools struggle with the complexity of human conversation, which includes:

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Lack of human validation

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Domain-specific context

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Sarcasm, irony, and mixed sentiment

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Conflated signals where brand perception masks CX breakdowns

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Language and dialect nuance, resulting in misclassification

The DataEQ difference

Our sentiment labelling is driven by the EQ Engine, a proprietary hybrid intelligence system that delivers accurate, structured data at scale.

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We apply advanced machine learning and natural language processing (NLP) to extract emotion from unstructured text, using large language models (LLMs) that have been trained and fine-tuned specifically for customer context. These models are continuously improved through real-world application and reinforced by expert human validation.

90%
accuracy level
 

Our sentiment labelling process consistently achieves 90%+ accuracy

<1%
margin for error
 

DataEQ’s margin of error for data labelling is typically below 1%

0.88
F1 score*
 

This reflects a strong balance of precision and recall across complex datasets

* Accuracy represents the agreement rate between DataEQ's labels and expert review. F1 score represents the balance between surfacing a label and applying it accurately. 

This level of statistical rigour gives data and CX teams the confidence to automate, analyse, and act without hesitation.

Net Sentiment, structured for action

We go further by splitting Net Sentiment into three targeted signals, ensuring you know where emotion is coming from and what action to take.

Overall Net Sentiment

Reflects total unsolicited sentiment, ideal for brand benchmarking.


Overall Net Sentiment gives organisations a clear, real-time view of how customers feel about their brand by measuring the balance of positive and negative conversation across online feedback. Unlike traditional survey-based metrics, it reflects unsolicited customer opinion at scale, making it a powerful indicator of brand health and customer experience.

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Operational Net Sentiment

Focuses on service, process, product, and support – driving CX insights.

A Net Sentiment score calculated using “operational” mentions, classified as conversation from people in the customer journey. Operational feedback is often systematically more negative, so separating it prevents CX issues from masking reputational strength (or vice versa) and gives a cleaner operational signal for service owners. A real-time indicator of friction and failure points, to diagnose root causes, prioritise fixes, and track whether operational changes lead to measurable improvements in customer sentiment.

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Reputational Net Sentiment

Tracks public-facing, brand-related mentions (e.g. ads, media, PR).

A Net Sentiment score calculated through a focus on “reputational” mentions, typically driven by campaigns, sponsorships, CSR, press/journalistic coverage, and general public commentary. It isolates brand image/PR equity from day‑to‑day service noise, enabling marketing/comms and risk teams to measure whether brand-led activity is building goodwill (or generating backlash) independent of operational execution. Evaluate campaign impact, monitor brand health, detect emerging reputation risks, and assess whether brand-led activity is generating sustained positive sentiment or reputational drag.

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Methodology: Calculating Net Sentiment

This simple, transparent method creates a standardised signal that’s comparable across time, brands, and regions.

Net Sentiment Calculation
Each data point is scored as positive (1), neutral (0) or negative (-1).

A score is then calculated by subtracting total negative conversation from the total positive conversation (Net Sentiment = positive conversation – negative conversation).

To calculate percentage Net Sentiment, this score is divided by the total number of posts (positive, neutral and negative) during that period.
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Sentiment labelling transforms unstructured text into structured, quantifiable data, turning messy feedback into actionable intelligence.

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The formation of Net Sentiment is an aggregated assessment metric that is a critical component in the quest for an authentic and complete voice-of-customer.

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This metric enables organisations to monitor public opinion, customer experience, brand perception, and emerging issues in real time.

Most sentiment models settle for "good enough."
 DataEQ aims for decision-grade.

By combining cutting-edge AI, calculated methodology, and human intelligence at scale, we deliver sentiment labelling that is not only accurate, but actionable, explainable, and built for enterprise intelligence.

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