Social Media Sentiment Analysis

Module 5 · Social Media

Social Media Sentiment Analysis

Analyze sentiment and merchant-related risk signals from social media to complement the reputational view used in KYB risk assessment.

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RFI module deep-dive

Social Media Sentiment Analysis

Prototype page exposes input contracts, processing stages, structured outputs, quality controls, privacy controls, integration points, PoC questions and evidence needed before production acceptance.

Open PoC Benchmark Lab

Inputs

Business profile URLsMerchant-supplied or verified business-facing social accounts

Merchant aliasesControlled terms for entity matching

Time windowConfigurable recent-period scope

Data policyBusiness-only / public-business signals default

Processing Pipeline

Access policy

Check approved API/access method and platform-specific constraints.

Account verification

Confirm the profile belongs to merchant or relevant business representative.

Content minimization

Filter to business-related content and configured time window.

Sentiment & signal NLP

Detect complaints, fraud indications, controversy and recurring themes.

Entity-aware aggregation

Avoid mixing similarly named merchants/accounts.

Privacy filter

Suppress unrelated personal data before storage/analyst display.

Structured Outputs

profile_matchConfidence merchant owns/relates to account

sentimentDistribution and trend

risk_themesComplaint / fraud / controversy signals

engagementOnly risk-relevant aggregate indicators

privacy_filterSuppressed fields / data classes

freshnessCollection window and last refresh

✓

Legally available social sourcesMonitor relevant social platforms only through approved and permitted access methods.

✓

Sentiment & risk signalsDetect complaints, suspected fraud, controversy and reputational signals.

✓

Bahasa IndonesiaSupport Indonesian language and local context.

✓

Data minimizationFilter personal data to what is necessary for merchant-risk evaluation.

#	Question to provider	Prototype status	Evidence / response expected
1	Which social platforms are supported and are they accessed through official APIs or other methods?	PoC response	Provide platform/access-method inventory and ToS compliance statement.
2	How does the solution ensure platform-terms and PDP compliance including processing basis?	PoC response	Show source governance, purpose limitation, minimization and retention.
3	What is sentiment-analysis accuracy for Bahasa Indonesia?	PoC response	Provide Indonesian benchmark and error analysis.
4	How are collected personal data retained and deleted?	PoC response	Provide retention schedule, deletion propagation and DSAR/correction path.

No open-ended surveillance

The module is scoped to merchant-relevant signals and does not create general behavioral profiles of individuals.

Official / permitted access

Platform-specific access rules are configured per connector and auditable.

Retention minimization

Raw posts/comments are retained only when necessary as decision evidence and for a defined period.

Sensitive inference controls

Do not infer protected/sensitive traits that are not required for KYB risk.

Illustrative structured output

Schema is intentionally explicit to support decision-engine integration, explainability and audit. Values are simulated.

{ "module":"social_sentiment", "accounts":[{"platform":"social_platform","profile_match":0.94}], "sentiment":{"negative":0.22,"neutral":0.56,"positive":0.22}, "risk_themes":[{"theme":"delivery_complaints","severity":"low"}], "privacy_filter":{"personal_fields_suppressed":14}, "requires_edd":false }

Digital Footprint Investigation

Business-profile identity resolution, sentiment, risk themes and source-governance controls.

Open Digital Footprint →