NLP and word processing

1) Why the NLP iGaming Platform

Support and retention: auto-classification of tickets, routing, ready-made answers.
Product and ASO: feedback analysis/release notes, monitoring the impact of updates.
Compliance and risk: PII/finance detection, RG signals, suspicious schemes.
Marketing/CRM: segmentation by topic/intention, generation of personal messages.
Knowledge search: quick access to provider FAQ/policies/rules, Q & A.
Operations: parsing the terms of shares, PSP limits, SLA partners.

2) Sources of texts and figs

Channels: tickets and support chats, App Store/Google Play, social networks/forums/telegrams, e-mail/web forms, internal wikis/policies, release notes of game and PSP providers, call/stream transcripts (ASR), PDF documents (OCR).

• Deduplication, bot/spam elimination
• language definition (ru/tr/es/pt/en/ka/...);
• reduction to UTF-8, normalization of emoji/slang/translite;
• metadata markup: channel, language, application/version, country, brand, game/provider, priority.

3) Privacy and PII edition (by default)

PII detection and revision: full name, phone numbers, e-mail, maps/IBAN, addresses, doc-ids.
Tokenization of identifiers (player_id→'u_tok_'), prohibition of raw PII in logs/features.
DSAR: quick search/deletion by subject token; Legal Hold - WORM log.
Geo/tenant isolation: storing text and keys in the license region.

4) Basic linguistics

Tokenization (including emoji/hashtags/emoticons) and sentence segmentation.
Normalization: lowercasing, removing diacritics (by language), correcting typos.
Lemmatization/stemming (ru/tr/es/pt/en), morphological labels (POS).
Stop words: language/domain-dependent lists (iGaming vocabulary should not be cut out).
Slang/jargon: dictionaries ("freespins," "wagering," "eating balance," "Papara," "withdraw pending").

5) Representations of text

Classics: n-grams, TF-IDF - fast baseline for classification/search.
Embeddings: multilingual transformers (sentence/dual encoders) → search, clustering, RAG, deduplication.
Domain-trained embeddings: additionally train on the body of support/reviews/policies → ↑relevantnost.
Hybrid: BM25 + Vector Search (ANN) → high coverage and accuracy.

6) Task class and examples

Classification: subject (payments, KYC, bonuses, provider, RG), seriousness, intention.
NER/RE: entities (PSP, providers, games, currencies, documents), relationships (provayder↔igra, PSP↔strana/metod).
Extraction of rules: parsing of bonus/wagering conditions, PSP limits (amounts, time, countries).

Summarization: tickets/threads/policies, "TL; DR for support and manager."

Q & A/knowledge search: answers from wiki/FAQ/regulations, explanations of RG/AML processes.
Moderation/toxicity: detection of profanity, threats, fraud.
Translation/localization: MT with domain glossary, post-edit.
ASR/OCR→tekst: letters, scans, calls, streams - into the analyzed text.

7) Retrieval and RAG (Retrieval-Augmented Generation)

Indexing: BM25 for "long tail," ANN (HNSW/IVF) for embeddings.
Chunking: 512-2048 tokens, with overlap; segmentation by sections/headings.
Rerankers: cross-encoder to improve the accuracy of the top k.
Citation: Source responses (id/title/wiki version).
Guardrails: banning "hallucinations" outside the hull; domain restriction.
Multilingualism: query in the user's language, documents in different languages → use multilingual embeddings.

8) Topics and aspects

Thematic modeling: BERTopic/LDA for discovery themes.
Aspect-based NLP: joint model of aspects and tonality (see the section "Sentiment analysis of reviews").
Aspect catalogue: payments/outputs/CCM/bonuses/crushes/localization/support/specific provider.

9) Moderation and risk

Toxicity/abuse: multilevel classification (offensive, hate, threat).
Fraud/social engineering: patterns "chargeback advice," "KYC bypass," links to gray schemes.
RG signals: frustration/aggression/self-restraint - into a separate channel and action policy.
Privacy: redaction before moderation; logs without PII.

10) Quality metrics

Classification/NER: Accuracy, macro/micro F1, per-class F1 (especially "rare" classes).
NER/RE: F1 @ span for entities, F1 @ rel for relationships.
Search: nDCG @ k, Recall @ k, MRR; for hybrids, the proportion of responses with quotes.
Summarization: ROUGE/BERTScore + human rubric (comprehensibility/accuracy/brevity).
RAG/Q & A: Exact/Partial Match, Faithfulness, Answer Rate.
Multilingualism: metrics by language/channel.
Operating system: p95 latency, cost/request, hit-rate cache,% Zero-PII in logs.

11) Architecture and pipelines

11. 1 Raw text → signal stream

1. Ingest (API/webhooks/parsers/OCR/ASR)

2. PII-redact → language → normalization (emoji/slang/tokens)

3. Embeddings/Features (Characteristics Catalog)

4. Tasks: Classification/NER/Tone/Moderation/Rule Extraction

5. Aggregations (Gold), alerts and dashboards

11. 2 Search/RAG

Index BM25 + vector; rerank, quotes, response cache; "minimum N documents" policy (k-anonymity).

11. 3 Serving

Online API for classification/search/Q & A; batch for reverse indexing/ASO analytics; stream for moderating chats/streams.

12) MLOps and operation

Registry models: version, date, training data, metrics, usage limits.
Shadow/Canary/Blue-Green releases; rollback on quality/ethics/latency thresholds.
Monitoring: vocabulary/language drift (PSI), latency, FP/FN toxicity, faithfulness RAG.
Cost management: caching of embeddings/responses, distillation/quantization, routing "light/heavy" model.

13) Integrations (use-cases)

Support: auto-triage of tickets (payments/CUS/bonuses), priority in severity, ready-made answers; translation with post-edit.
Product/Dev: clustering of bug reports, summation of threads, extraction of "crash patterns" (model/OS/game).
Marketing/ASO: retrieving "1" reasons, generating FAQs/status banners.
RG/Compliance: automatic routing of sensitive cases, toxicity control.
Operations: parsing of provider rules/PSP limits, alerts when wording changes.

14) Templates (ready to use)

14. 1 Inference Policy (SLO/Privacy)

yaml nlp_service: texts. core slo:
p95_latency_ms: 250 success_rate: 0. 995 privacy:
pii_redaction: true min_group_size: 20 monitoring:
drift_psi_max: 0. 2 faithfulness_min: 0. 9 # for RAG responses

14. 2 "Gold: nlp_events" scheme

yaml timestamp: TIMESTAMP brand: STRING country: STRING lang: STRING channel: STRING     # appstore, support, social, faq, policy topic: STRING      # payments, kyc, promo, provider, rg,...
sentiment: STRING    # neg/neu/pos toxicity: STRING     # none/low/med/high entities: ARRAY<STRUCT<type STRING, text STRING, norm STRING>>
actions: ARRAY<STRING>  # routed_to_support, faq_update, rg_notify source_id: STRING    # trace/корреляция

14. 3 Example of DSL rule (alert to risk lexicon)

yaml rule_id: rg_escalation_lang source: stream:nlp_events when:
topic: ["rg"]
toxicity: ["med","high"]
sentiment: ["neg"]
lang: ["ru","tr","es","pt"]
confirm: {breaches_required: 2, within: PT10M}
actions:
- route: pagerduty:rg
- create_case: {type: "rg_review", ttl: P14D}
privacy: {pii_in_payload: false}

14. 4 Domain vocabulary catalog (fragment)

yaml glossary:
payments: ["deposit","withdraw","Papara","Mefete","chargeback","KYC","IBAN"]
promo: ["bonus","freespins","wagering","cashback","RTP"]
rg: ["self-exclusion","limit","cooldown","loss streak"]
provider: ["Pragmatic Play","NetEnt","Spribe","Hacksaw"]

15) Success Metrics (Business/Operations)

Support: auto-routing without escalation, MTTA/MTTR,% of "correct" macros.
ASO/NPS: SI/tonality correlation with rating and retention.

Compliance: zero PII leaks; SLA DSAR; Proportion of correct RG routings

Search/RAG: proportion of responses with citations, time to response, agent satisfaction.
Cost: $/1k requests, hit-rate cache, distillation savings.

16) Implementation Roadmap

0-30 days (MVP)

1. Ingest support and reviews, PII edition, language/normalization.
2. Baselines: classification of topics, tonality, toxicity (multilingual models).
3. Hybrid search (BM25 + vector) by FAQ/policies; RAG with quotes.
4. Dashboards SLO/quality; Zero-PII in the logs.

30-90 days

1. NER/RE for PSP/providers/bonus rules; extracting limits.
2. Aspect-based SA, ticket summarization, auto-responses (HITL).
3. Shadow→canary releases, monitoring lexicon/language drift.
4. Moderation of streams/chats in realtime; RG alerts/payments.

3-6 months

1. Domain-trained embeddings, distillation; budgets by value.
2. Auto-generation of references/FAQ/e-mail templates from RAG.
3. Parsing of contracts/release notes of providers, alerts when conditions change.
4. External privacy audit and regular hygiene sessions of dictionaries/aspects.

17) Anti-patterns

Logs/dashboards with PII; translation into sandboxes without editing.
"One size" for all languages/channels; ignore slang/emoji.
Q&A without citation of sources (hallucinations).
Manual triage of tickets "forever" - without auto-classification and SLO.
Model without monitoring drift/ethics and rollback plan.

18) Related Sections

Feedback Sentiment Analysis, Analytics and Metrics APIs, DataOps Practices, MLOps: Model Exploitation, Anomaly and Correlation Analysis, Data Stream Alerts, Access Control, Retention Policies, Data Ethics and Transparency.

Total

NLP is a production pipeline of Safe Injection, Language and Domain Normalization, Quality Embeddings and Tasks (Classification/NER/RAG), Observability, and SLO. In iGaming, he translates chaotic text from reviews, chats, documents and streams into solutions: faster support, transparent compliance, predictable releases and clear rules for the player.