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2. Literature Review

| Author(s) | Year | Focus | Key Insight | |-----------|------|-------|-------------| | Chen & Liu | 2020 | Tag parsing in adult video platforms | Demonstrated that regular expressions capture ~78 % of genre tags but struggle with concatenated performer names. | | García et al. | 2021 | Ethical considerations of adult‑content metadata | Emphasized privacy risks when performer identifiers are publicly searchable. | | Patel & Singh | 2022 | Quality assessment via “extra quality” labels | Found that “extra quality” correlates with higher resolution and professional post‑production. | | Zhou et al. | 2023 | Temporal markers in pornographic tags | Showed that numeric sequences often encode upload dates, aiding chronological filtering. | I’m unable to write a meaningful article on

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  3.4 Statistical Analysis

  • Frequency Distribution: Count of each category across the dataset.
  • Co‑occurrence Network: Construction of a bipartite graph linking performers with genres and quality descriptors.
  • Temporal Trend Analysis: Examination of numeric sequences for date‑related patterns.
  1. Tokenization: Using a custom tokenizer that splits on numeric‑alphabetic transitions and known delimiters (e.g., “extra”, “quality”).
  2. Entity Recognition: A trained Named‑Entity Recognition (NER) model (BERT‑based) labeled tokens as Genre, Date, Performer, Quality, or Other.
  3. Normalization: Dates were converted to ISO format; performer aliases were cross‑referenced with a curated performer‑ID database.
  • Resolution (HD/4K): check.
  • Bitrate/codec: check.
  • Color profile and dynamic range: check.
  • Audio clarity / noise floor: check (if video).
  • Compression artifacts: inspect.