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This dashboard augments quantitative usage analytics with qualitative insight—surfacing what people talk about, how they feel, what they search (but cannot find), and why churn or satisfaction shifts are emerging.
Sentiment Analysis has moved. The sentiment analysis capabilities described here have been expanded into a dedicated feature with AI-powered topic detection, per-topic summaries, and thread-level analysis. See Sentiment Analysis for the new experience.

Topic Mentions

Volume & share of conversation

Sentiment

Emotional direction & skew

AI Summaries

Condensed thematic reasoning

Recommendations

Actionable remediation & leverage

AI Research

Ad‑hoc natural language analysis

Search Intent & Gaps

Unmet demand & content prioritization

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What Can You Answer in Minutes?

Which features generate praise vs friction and their emerging pain themes.

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Daily Operational Workflow

1

Open & Filter

Select date range, locale, platform/app segment, and (optionally) user cohort (new vs returning) to focus analysis.
2

Scan Topic Shifts

Compare Top Mentions list vs prior period % change to flag emerging themes.
3

Assess Sentiment

Check Overall Sentiment gauge; drill into topics with highest negative skew.
4

Read AI Summaries

Skim negative → neutral → positive summaries to capture root causes & delights.
5

Review Recommendations

Validate AI suggestions; convert high-confidence items into backlog tickets (label with source tag).
6

Run Research Query

Pose a targeted natural language question for deeper synthesis (e.g., “Main complaints about video uploads this week”).
7

Evaluate Search Gaps

Open Search Insights tab; prioritize new content for highest Gap Index terms.
8

Log Actions

Record chosen remediations with owner & ETA; schedule follow-up check next cycle.

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Community & Topic Filtering

Refine any insight to the communities and topics that matter—filtering removes noise so trend, sentiment, and gap signals stay actionable.

Topic Filter

Focus on specific themes

Community Filter

Isolate audience segments

Combined Scope

Intersect topic + community for precision

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Why Use Community-Level Filters?

  • Targeted insights: Each community exhibits distinct engagement & sentiment patterns.
  • Faster diagnosis: Narrow scope to confirm whether an issue is broad or localized.
  • Segment strategies: Tailor interventions (content, moderation, onboarding) per cohort.
  • Reduce noise: Avoid dilution from mega-communities overshadowing smaller niches.

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Getting Started

Need Social Insights enabled? Email support@social.plus with your tenant ID.

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Interface Tour

All communities & topics aggregated (e.g., total mentions count across full corpus) until any filter is applied.
Apply topic first to isolate theme, then add community filter to test audience-specific variance.
If results feel sparse, clear the most recently added filter first to widen context.

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Applying Filters (Step-by-Step)

1

Open Topic Menu

Click Topic: Select topic; search or scroll the list.
2

Select Topic(s)

Choose one (start simple) or multi-select to form a thematic cluster.
3

Observe Metrics

Note changes in Total Mentions, sentiment bars, emerging topics list.
4

Add Community Filter

Open Community: Select community; search e.g. “Fashion & Style”; select.
5

Compare Before/After

Is sentiment skew or gap profile materially different? Capture delta.
6

Iterate

Test additional communities to spot outliers; avoid over-filtering below statistically meaningful volume.

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Interpreting Filtered Results

If filtered mentions fall below reliability threshold (internal baseline), treat insights as directional only.

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Best Practice Patterns

Apply topic FIRST to anchor analysis, then layer community to avoid premature fragmentation.
Keep an unfiltered baseline tab open for quick relative comparisons.
Set a minimum mentions threshold (e.g., 300) for acting on sentiment shifts.
Record filter combinations used when generating recommendations to ensure reproducibility.
Once per week, review global view to avoid tunnel vision from niche segments.

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Example Workflow

  1. Start unfiltered → identify emerging topic.
  2. Apply topic filter → confirm growth & sentiment skew.
  3. Add top 3 related communities → detect which exhibits worst negative skew.
  4. Run AI Research prompt: “Root causes of negative sentiment about <topic> in <community>.”
  5. Validate references → create targeted remediation ticket.
Combining topic + community filters early in investigation accelerates root cause isolation but always validate sample size before action.

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Modules & Interpretation

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Mentions & Topic Breakdown

Highlights conversation concentration. A healthy distribution usually shows a balanced long-tail—extreme concentration may indicate a blocking issue OR a successful campaign.

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Trend Graph

Multi-line frequency lines reveal acceleration or decay. Sustained upward slope + positive sentiment → amplify; upward + negative → triage.

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Sentiment (Overall + Per Topic)

Overall gauge (0–100). Correlate dips with deployment timestamps or incident reports. Topic sentiment bars isolate outlier issues masked by aggregate positivity.

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AI Post Summaries

Condense thousands of posts into 5–10 key statements per sentiment polarity. Treat as directional—spot check references before broad decisions.

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AI Recommendations

Strategic remediation / opportunity list derived from the weighted intersection of volume, sentiment skew, and recency. Confidence score (if shown) reflects model certainty; low-confidence items may require manual verification.

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AI Research (Conversational Analyst)

Natural language Q&A across indexed conversations. Returns a structured mini-report: Introduction, Methodology, Findings, References (canonical post links) to enable traceability.

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Search Insights & Content Gaps

Maps expressed intent (search queries) vs existing content coverage. Gap Index ranks unmet demand to drive documentation & self-service improvements.

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Metrics & Signals

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Leveraging AI Research Effectively

”Why did sentiment drop after release 5.2?"

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Prompt Patterns

”Compare sentiment for video uploads vs live streaming this month."
"Summarize primary reasons behind negative sentiment about notifications."
"Differences in topics between new and returning users last 14 days."
"What changed in top 5 topics compared to previous week?"
"Identify feature improvement opportunities mentioned positively but low volume.”

Sentiment Analysis

AI-powered sentiment scoring, topic detection, and thread-level analysis

Activity Analytics

Quantitative usage metrics

Raw Data Export

Deeper custom analysis

User History

Drill into individual behavior
Need advanced taxonomy tuning, extended retention windows, or custom sentiment domains? Contact support for enhanced AI configuration options.