particle_podcast_get_share_of_voice

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Split a person's, company's or entity's podcast mentions by who said them: the speakers' occupations (SOC 2018) or fields of research (ANZSRC 2020). Answers "who is talking about NVIDIA — investors, engineers, journalists?" in one call: each group's mention lines, share, distinct speakers and episodes over a window (default the last 30 days, at most 365), optionally bucketed by day, week or month. `speakers="guests"` leaves hosts out; `level="detailed"` splits into detailed occupations or ANZSRC groups; `podcast_slug` or `publisher_slug` narrows to one show or network. `keyword_search` splits the lines that say a word or quoted phrase instead of an entity's mentions (who talks about prior authorization?), counting a line only when its own speaker said every word in it. Its window is at most 90 days, and a word said in too many episodes is refused with a way to narrow it. This is the per-speaker answer that `particle_podcast_get_episode_timeseries` with `keyword_search` and `guest_occupation` cannot give: that counts matching segments in episodes with such a guest, whoever spoke them. Shares are of the mentions spoken by someone with an expertise read, and a person listed under several codes counts in each, so shares can sum past 100%. Each group's code is a `guest_occupation` (or `guest_field`) value: pass it to `particle_podcast_find_mentions` with the same entity to read what that group said. For a keyword, `particle_podcast_search_transcripts` with the same `keyword_search` finds candidate passages: its guest filters keep segments with such a guest, so check each matching line's speaker, and host lines are not reached that way. Not for the mention lines themselves (`particle_podcast_find_mentions`) or for a brand's mention volume over time and the shows carrying it (`particle_podcast_get_mention_trends`).