> ## Documentation Index
> Fetch the complete documentation index at: https://vicewire.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Directional Layers
> How ViceWire attaches directional interpretation and FinBERT scoring to each event object.
ViceWire does not reduce an article to one broad positive or negative sentiment label. Directional analytics are attached to individual event objects and sit alongside event family, stage status, subtype, metadata, and affected business surface.
## Two layers
### 1) Event-attached directional interpretation
**Directional interpretation** answers two simple questions:
1. Could this event be favorable for the entity?
2. Could this event be adverse for the entity?
Directional interpretation is attached to the event object rather than assigned once at the article level for two reasons:
Direction is derived within the logic of the specific event family.
For example, a favorable legal outcome is derived differently from a favorable competitive position.
One article can contain a favorable demand signal for iPhone while also containing an unfavorable geopolitical development such as armed conflict. Storing direction inside each event object keeps those implications separate instead of flattening them into a single document-level view.
These directional fields are part of the event-extraction layer. They capture how a specific event may matter for an entity within the logic of that event family, not just the overall tone of the article.
### 2) FinBERT
Each event object can also carry a separate `finbert` layer.
[FinBERT](https://huggingface.co/ProsusAI/finbert) is an open-source finance-specific NLP model.
In ViceWire, `finbert` is computed from event-linked evidence associated with the confirmed event family. The model produces an aggregate tone for that event-linked evidence and also derives supporting diagnostics from the same evidence set.
This means `finbert` should be read as an event-linked tone layer rather than a broad document-level sentiment label.
`finbert` is organized into:
`tone` stores the aggregate FinBERT output for the event-linked evidence.
It is best interpreted as the overall tone of the evidence tied to that event, not as a broad document-level sentiment label.
`tone_distribution` stores the percentage makeup of the scored event-linked sentences across negative, neutral, and positive classifications, plus the total number of scored sentences.
`tone_extremes` stores the strongest single-sentence probability observed for each label across the scored event-linked evidence.
It is best interpreted as the most extreme negative, neutral, and positive signal present in the event-linked evidence.
Overall, `finbert` should be used in conjunction with [event-attached directional interpretation](#1-event-attached-directional-interpretation), not as a replacement for it.
## Event context and mixed sentiment implications
Text tone and event meaning are not the same thing, for example:
* A partnership can read as **neutral** in tone when the article is largely descriptive, while still carrying a meaningful **favorable** implication if the partnership enables product capability or distribution.
That is why ViceWire does not rely on a single document-level sentiment label or a simple positive-versus-negative event tag.
## Simplified Example
In early 2026, articles reported that Apple was planning a major Siri revamp into a built-in conversational AI chatbot across iPhone, iPad, and Mac. The event is framed as part of Apple’s attempt to recover ground in generative AI after a weak Apple Intelligence rollout and to make Siri more competitive as a core product capability.
In one article, Google and OpenAI appear as competitor references because the article presents them as the main external benchmarks and pressure points. OpenAI is positioned as a direct AI threat through ChatGPT and its broader platform ambitions, while Google is framed both as a competitive benchmark through Gemini and as a technical dependency because Apple is said to be relying heavily on Google-developed models for the Siri upgrade.
This article touches on multiple L1 event families. If we focus solely on the **Competition & Market Structure**, the competitive read is mixed. To capture that, the **directional layer asks two simple questions**:
1. Does the article explicitly support a materially favorable competition or market-structure implication for Apple? (`material_favorable_implication_present`)
2. Does the article explicitly support a materially adverse competition or market-structure implication for Apple? (`material_adverse_implication_present`)
In this example, the model answers `true` to both questions. At the same time, FinBERT gives the event-linked evidence a `negative` **tone** reading.
Both outputs are plausible because the article frames Apple as behind in the AI race, which supports the adverse side. But it also describes Apple taking a concrete step to accelerate Siri and Apple Intelligence capabilities, which supports a favorable strategic interpretation within the competitive landscape.
```json Example Payload: Apple Siri Gemini Integration theme={null}
{
"event_family": "competition_market_structure",
"event_family_metadata": {
"material_favorable_implication_present": true,
"material_adverse_implication_present": true,
"competitor_reference": ["Google", "OpenAI"]
},
"finbert": {
"tone": {
"negative": 0.37,
"neutral": 0.31,
"positive": 0.32,
"label": "negative"
},
"tone_distribution": {
"negative_sentence_share": 0.43,
"neutral_sentence_share": 0.29,
"positive_sentence_share": 0.29,
"sentence_count": 7
},
"tone_extremes": {
"top_negative_sentence_prob": 0.96,
"top_neutral_sentence_prob": 0.87,
"top_positive_sentence_prob": 0.78
}
}
}
```
See the full Apple Siri Gemini Integration Payload Example
## The core idea
Directional layers in ViceWire are additive. They help interpret an event, but they do not replace event family, stage, subtype, metadata, or affected business surface.
They should be read as structured interpretation layers, not as guaranteed predictors of market reaction.