> ## Documentation Index
> Fetch the complete documentation index at: https://docs.particle.pro/llms.txt
> Use this file to discover all available pages before exploring further.

# particle_company_get_statement_characteristics

> How a company's people speak on podcasts, together, and how that moves: their lines' sentiment, certainty, specificity and temporal orientation as shares, per week or month.

How a company's people speak on podcasts, together, and how that moves over time. It reports the share of their lines on each axis:

* **Sentiment:** negative, neutral or positive.
* **Certainty:** hedged, neutral or confident.
* **Specificity:** vague, neutral or specific.
* **Temporal orientation:** about the past, the present or the future.

Each axis is shown for the whole window (the last year by default) and per week or month, with how many of the people spoke. Use it for questions like "how confident do OpenAI's people sound lately?" or "are Microsoft's executives talking more about the future?". The labels are the ones [speaker statement characteristics](https://docs.particle.pro/podcasts/speaker-statement-characteristics.md) puts on transcript lines.

The people are the ones [`particle_company_get`](https://docs.particle.pro/mcp/tools/companies/company-get) lists with `include: ["people"]`: those in a current role by default, and those in past roles too with `include_past_roles`. Shares are of their **scored lines**: lines they spoke, in any role, that carry characteristics. Lines too short to read carry none. `low_sample` marks a share with fewer than 20 scored lines behind it.

For one person, use [`particle_person_get_statement_characteristics`](https://docs.particle.pro/mcp/tools/people/person-get-statement-characteristics); every example names its speaker's `person_slug` for it. Resolve the company first with [`particle_company_resolve`](https://docs.particle.pro/mcp/tools/companies/company-resolve).

## Inputs

| Field | Type | Required | Default | Description |
| - | - | - | - | - |
| `company_slug` | string | yes | — | Company slug from `particle_company_resolve` (e.g. `"microsoft"`), domain (e.g. `"microsoft.com"`), or canonical ID. |
| `include_past_roles` | boolean | no | `false` | Count people in past roles at the company too. |
| `published_after` | string | no | a year before `published_before` | Start of the window (ISO 8601 date or date-time). |
| `published_before` | string | no | now | End of the window. A bare date includes that whole UTC day. |
| `interval` | enum | no | `month` | `week` (weeks start on Monday, UTC) or `month`. |
| `include` | array of enums | no | `[]` | `examples` adds each bucket's strongest line for every outer label (negative/positive, hedged/confident, vague/specific, past/future), with who said it, its episode and a link that plays it. |

## Output

A markdown document headed `## How <company>'s people speak`, laid out like [`particle_person_get_statement_characteristics`](https://docs.particle.pro/mcp/tools/people/person-get-statement-characteristics) with one extra row:

* `**People:**` says how many of its people said scored lines in the window.
* The `**Window:**`, `**Scored lines:**` and `**Characteristics version:**` rows follow.
* `### Overall` lists each axis's label shares.
* `### By month` (or `### By week`) is a zero-filled table, with `(partial)` and `(low sample)` marks.
* With `include: ["examples"]`, each bullet under `### Strongest lines, <bucket start>` names the speaker: `**label** (axis): Name (person_slug …): "text" — Episode on Podcast (episode_slug …) <link>`.

With `output_format=json`, the response is the REST body of `GET /v1/companies/{id}/statement-characteristics`:

* `company`, `current_only` and `people`, and the person breakdown's fields: `scored_lines`, `episodes`, `low_sample`, `characteristics_version`, `characteristics`.
* `buckets`: each also carries `people`, and each example a `speaker`.

Reads are slower for the largest companies, about 3–4 s for one with \~800 people.


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