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🏆 BERTopic v22 - THE COMPLETE MASTERPIECE

Mejoras sobre v21:

  1. MODELO GUARDADO: SafeTensors para reutilización
  2. TODAS LAS VISUALIZACIONES: DataMapPlot, Hierarchy, Heatmap, etc.
  3. MÉTRICAS MATEMÁTICAS: Coherence (UMass, NPMI), Diversity, Silhouette
  4. TOPICS OVER TIME: Análisis temporal
  5. HIERARCHICAL TOPICS: Árbol jerárquico
  6. GET_DOCUMENT_INFO: Metadata completa
  7. REDUCE_OUTLIERS: Reducción inteligente

Métricas Matemáticas:

  • Topic Diversity: 0.6995305164319249
  • Silhouette Score: 0.5787373781204224
  • Mean NPMI Coherence: 0.0
  • Outlier Ratio: 0.14259485924112608

Archivos Generados:

  • modelos/topic_model_v22/: Modelo serializado (SafeTensors)
  • visualizaciones/: HTMLs interactivos (DataMapPlot, Hierarchy, etc.)
  • metricas/: JSON con todas las métricas
  • document_info_complete.xlsx: Metadata por documento
  • hierarchical_topics.xlsx: Estructura jerárquica
  • topics_over_time.xlsx: Evolución temporal

Cómo Cargar el Modelo:

from bertopic import BERTopic
topic_model = BERTopic.load("modelos/topic_model_v22")

Fórmulas Matemáticas Usadas:

UMass Coherence (Mimno et al., 2011)

C_UMass = (2 / (N * (N-1))) * Σ log((D(w_i, w_j) + ε) / D(w_j))

NPMI Coherence

NPMI(w_i, w_j) = log(P(w_i, w_j) / (P(w_i) * P(w_j))) / (-log(P(w_i, w_j)))

Topic Diversity (Dieng et al., 2020)

TD = |unique_words| / (|topics| * top_n)

Silhouette Coefficient

s(i) = (b(i) - a(i)) / max(a(i), b(i))

Davies-Bouldin Index

DB = (1/k) * Σ max_{j≠i}((σ_i + σ_j) / d(c_i, c_j))

Timestamp: 2026-01-14 16:26:35.120866

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