📋 Available on: Growth, Enterprise, Agency, and Custom plans
Sentiment Analysis classifies the emotional tone of user-generated content captured by Archive. Each post is reviewed by a brand-aware model and assigned a sentiment label that appears in content views, filters, and campaign reports.
How Sentiment Analysis Works
Each piece of UGC is classified into one of five categories:
Positive — Content expressing favorable opinions, enthusiasm, or satisfaction
Neutral — Informational or factual content without evident emotion
Mixed — Content containing both positive and negative signals
Negative — Content expressing criticism, frustration, or dissatisfaction
Not Applicable — Content where emotional sentiment is not relevant or cannot be detected (e.g. silent videos, music-only audio)
Classification is brand-aware. The model considers the context of your brand category when scoring sentiment — for example, "this workout was brutal" is classified as positive for a fitness brand, rather than negative based on the word alone.
Where to Find Sentiment Analysis
Individual Content View
When viewing a single post in Social Listening, the sentiment label is shown alongside the content.
Content Filtering
On the Social Listening content page, you can filter by one or more sentiment categories to surface specific subsets of content — for example, only Positive posts for ad selection, or only Negative posts that require review.
Reporting Dashboard
Campaign Reports include a sentiment time-series chart showing the daily breakdown of posts by category (Positive, Neutral, Mixed, Negative, Not Applicable). The chart can be filtered by campaign and date range.
Campaign Reports — Sentiment Breakdown
Campaign reports also include a sentiment section with two views: a time-series chart showing the daily distribution of posts by category and a set of totals tiles (Total, Positive, Neutral, Mixed, Negative, Not Applicable) showing aggregated counts for the full campaign period. Clicking any tile opens the underlying content filtered to that sentiment category.
Use Cases
Ad content selection — filter for Positive sentiment to identify UGC suitable for paid campaigns.
Issue detection — track Negative sentiment spikes to surface emerging product or messaging issues quickly.
Brand perception tracking — monitor sentiment distribution over time to measure how launches and campaigns affect audience response.
Creator performance comparison — compare sentiment distribution across creators to identify which ones consistently drive favorable responses.
Limitations
Filtering campaign content by sentiment is not yet supported in campaign views.
The model does not distinguish whether sentiment is directed at the brand or at an influencer featured in the content.
Historical sentiment processing for content captured before the feature was enabled is available on request, not automatic.
Common Questions
Do I need to configure anything to use Sentiment Analysis?
No. On supported plans, Sentiment Analysis runs automatically on all newly captured content. No setup is required.
Why is some of my older content not classified?
Sentiment is applied to content captured after the feature was enabled for your workspace. Historical content can be processed retroactively on request — contact Archive Support.
How accurate is the classification?
The model is brand-aware and considers your brand category when scoring. Accuracy is highest on content with clear text or transcripts. Content with no spoken audio, no caption, or limited context is more likely to be classified as Not Applicable.