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Key phrases are stored within the facet:nlp_tag__phrases.
The item’s Title is also added.
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With configuration tag_topics:True, the pool of ranked key-phrases is used to extract cleaned, deduplicated phrases referred to as “topics” (stored in facet:nlp_tag__topics).
Concept
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- Filter steps:
- Remove terms with POS ["ADJ", "DET", "PUNCT"]
- Remove terms containing (almost) only number characters, like `33120x`
- De-Duplicate:
- Skip phrases that are also detected in NER-TAGS ["PRODUCT", "EVENT", "PERSON"] (configurable)
- Skip phrases that contain terms from already stored "topics"
- Select 20 phrases evenly across all ranks (as determined via TextRank) |
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Enrichment
Overall Sentiment Tagging Label
facet:sentiment_pretrained
One sentiment label (neutral, positive, negative) per document.Sentiment analysis is applied per sentence
Sentences with neutral sentiment are skipped
Overall Sentiment Score
facet:nlp_tag__sentiment_score
Float value within [-1,+1]Sentiment Assessment
facet:positive_terms, facet:negative_terms
A sentiment phrase consists of the valence-term and it’s context. \
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