Sentiment trends in AI show how the tone of brand mentions in answers—positive, neutral, or negative—shifts over time by platform and topic. Watching the trend line gives us trajectory and helps us understand whether a content update, a launch, or a PR moment improved perception where it matters.
Why we track trends, not just snapshots
Point in time sentiment can be noisy. A single answer might tilt negative because it quotes an old article. Trend lines filter out noise and reveal whether changes persist. They also show which platforms move first. Retrieval centric experiences often reflect updates faster than assistants that depend more on training.
What we analyze week to week
We look at the rolling distribution of positive, neutral, and negative tone by platform and topic tag. We note deltas between platforms. We check competitor movement so we know if a change is category wide or specific to us. When the line shifts, we always read the underlying answers and citations so we understand the claims driving the change.
How this informs action
If sentiment drifts neutral in evaluative prompts, we add “best for” guidance and pull proof into the TLDR. If it turns negative around a feature, we publish a clear update note or an FAQ. When phrasing improves in a third-party source, we contribute an expert quote or provide data so the narrative repeats consistently.
How we run it in the platform
Our dashboards trend sentiment by platform and tag. We tag content and PR events on the timeline and export a monthly view for stakeholders with a short narrative and next steps. We validate improvements by confirming that the positive shift persists across at least two measurement cycles.