Sentiment

Overview The Sentiment metric measures the tone of discussion around a keyword, brand, or topic across social and web data. It classifies posts as positive, negative, or neutral, and aggregates them into a distribution and a single net sentiment score. This helps quantify how people feel, not just how much they are talking.

Historical Length Varies by platform and data availability, but typically spans multiple years of historical data where supported. Allows users to track how perception evolves over time, especially around product launches, updates, or events.

Granularity Available at daily aggregation, with the ability to view sentiment snapshots tied to specific time windows or spikes in discussion.

Update Frequency Sentiment data is updated daily, reflecting the most recent shifts in tone as new posts and discussions are ingested.

Methodology Sentiment is derived using natural language processing models that analyze the tone and context of posts. Each post is classified into one of three categories:

• Positive: Favorable or supportive language

• Negative: Criticism, complaints, or dissatisfaction

• Neutral: Informational or non-opinionated mentions

These are then aggregated into:

• Percentage breakdown (Positive / Negative / Neutral)

• Net Sentiment Score (range: -1 to +1)

The score is calculated by weighting positive vs. negative mentions, providing a directional view of overall sentiment.

Use Cases

• Identify user frustration or product pain points early

• Track consumer perception shifts after launches or updates

• Validate whether rising discussion is positive or negative

• Compare brand perception vs. competitors

• Support earnings or demand narratives with qualitative context

Example Visualization

In the example above:

• P: 24% | N: 58% | U: 18% | Score: -0.33

This indicates:

• Majority of conversation is negative

• Overall tone is skewing unfavorable

• Likely driven by complaints or friction around the topic