Introduction to Sentiment Analysis

Sentiment Analysis in Delphi research goes beyond simply collecting ratings or probabilities. It helps you capture the emotional and cognitive patterns that drive experts' judgments.

When people assess future scenarios, they don't only think rationally. Their feelings, experiences, and worldviews also shape how they respond. Sentiment analysis makes these underlying factors transparent and measurable.

Why Use Sentiment Analysis?

• Adds emotional and psychological depth to your results
• Reveals how optimism, pessimism, or professional experience influence expectations
• Highlights hidden biases or motivational factors
• Enables more nuanced scenario interpretation

In short: Sentiment Analysis helps you understand why experts think the way they do.

Dimensions of Sentiment Analysis

In Delphi studies, sentiment can be analyzed across several key dimensions:

1. Level of Optimism / Pessimism

Definition:

How positive or negative experts feel about future developments.

How it's measured:

You can ask participants to rate their general level of optimism about a topic (e.g., on a scale from 1 = very pessimistic to 5 = very optimistic).

Why it matters:

Optimistic participants often estimate higher probabilities for favorable scenarios.

Pessimistic participants may be more conservative or risk-aware.

Practical Example:

Suppose you have this scenario:

"AI will fully automate 50% of current jobs by 2040."

You calculate the mean optimism rating across all experts (e.g., 3.2 on a 1–5 scale). You then split participants into groups:

• Optimists: Those scoring above 3.2
• Non-Optimists: Those scoring 3.2 or below

Interpretation:

If Optimists give this scenario an average probability of 80%, but Non-Optimists only 40%, this shows a strong sentiment effect.

Note:

This procedure is similar to Stakeholder Group Analysis, but here the grouping is based on psychological traits, not professional affiliation.

2. Level of Experience

Definition:

How experienced or knowledgeable experts are regarding the topic.

How it's measured:

Through self-reported experience levels (e.g., beginner, intermediate, expert).

Why it matters:

More experienced participants may judge scenarios differently – sometimes more realistically, sometimes more critically.

Example:

• Experts with >10 years in AI rate automation likelihood at 60%.
• Less experienced experts rate it at 85%.

This signals that experience moderates expectations.

3. Positive and Negative Affect Schedule (PANAS)

Definition:

The Positive and Negative Affect Schedule (PANAS) measures how much positive or negative emotion someone feels while evaluating scenarios.

Why it matters:

A high negative affect might bias judgments toward pessimism, while positive affect may fuel optimism.

How it's measured:

Participants rate statements like:

• "I felt excited when thinking about this scenario."
• "I felt anxious about this scenario."

Example:

Experts with high negative affect may consistently rate scenarios as less probable or more threatening.

How Sentiment Analysis Is Done – Effortlessly with Durvey.org Analytics

Understanding how emotions and attitudes influence expert opinions is one of the most powerful aspects of the Delphi method.

With Durvey.org, you don't need to do any manual calculations or complex coding – the entire sentiment analysis process is automated for you. As soon as your Delphi round is completed, Durvey.org generates clear subgroup comparisons, descriptive statistics, and even ready-to-publish report text.

Here's exactly how it works in practice:

1. Collect Sentiment Data Alongside Scenario Ratings

When you design your Delphi survey in Durvey.org, you can easily add sentiment measures to each scenario, such as:

• Optimism scales (e.g., 1–5)
• Experience level questions
• Standardized affect items (e.g., PANAS: Positive and Negative Affect Schedule)

Durvey.org automatically records and links all these variables to each participant's responses.

2. Compute Descriptive Statistics – Instantly

After data collection closes, Durvey.org immediately calculates:

• Mean and median optimism scores
• Average positive and negative affect
• Standard deviations for each sentiment variable

You don't need to export data or create formulas – all summaries are generated for you in a clean dashboard.

3. Split Participants into Subgroups Automatically

Durvey.org dynamically creates subgroups to reveal patterns in your data:

• Optimists vs. Non-Optimists: Based on whether their optimism score is above or below the mean.
• High vs. Low Experience: According to self-reported expertise.
• Positive vs. Negative Affect Profiles: To see how mood relates to judgments.

You never have to define cut-off values yourself – Durvey.org does it in one click.

4. Compare Scenario Ratings Between Groups

Next, Durvey.org runs all relevant comparisons:

• Do Optimists rate scenarios as more likely?
• Do highly experienced experts show stronger consensus?
• Does negative affect correlate with pessimistic estimates?

Interactive tables and charts let you explore these insights visually or export them for publication.

5. Interpret and Generate Paper-Ready Reports

Finally, Durvey.org produces ready-made analytical text you can use in your reports or presentations.

This includes:

• A clear interpretation of the statistical findings
• Highlights of potential emotional drivers of opinions
• Notes about possible biases (e.g., optimism bias, affective forecasting errors)

You can copy this text directly into your paper, saving hours of manual analysis and writing.

Example Walkthrough

Here's what it looks like in a real scenario – all handled automatically by Durvey.org:

Scenario:

"Universal Basic Income will be adopted globally by 2040."

Step 1: Collect Ratings and Sentiment

• Probability Rating (0–100%)
• Optimism (1–5)
• Positive Affect (1–5)
• Negative Affect (1–5)

Step 2: Compute Means

• Mean Optimism = 3.4 (auto-calculated)

Step 3: Group Participants

• Optimists > 3.4
• Non-Optimists ≤ 3.4

(Durvey.org flags these groups for comparison.)

Step 4: Compare Estimates

• Optimists' mean probability = 70%
• Non-Optimists' mean probability = 35%

Step 5: Interpretation (auto-generated text)

"The analysis indicates that optimism strongly shapes probability estimates, suggesting a potential optimism bias among participants. Researchers should consider this emotional influence when interpreting the results."

With Durvey.org Analytics, you never have to build these analyses from scratch. Everything – from subgroup splits to clear narrative explanations – is created for you automatically.

Why Sentiment Analysis Matters

Sentiment analysis helps you:

• Explain variance in Delphi results
• Understand expert psychology
• Identify emotional biases
• Improve the transparency of forecasts

It provides rich insights that purely quantitative ratings often miss.

Conclusion

Sentiment Analysis adds a human layer to Delphi research. By systematically examining optimism, experience, and affect, you can:

• Detect hidden patterns.
• Make more accurate interpretations.
• Communicate results with more credibility.

This approach empowers your team and stakeholders to understand not just what experts believe, but why they believe it.