% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{speeches}
\alias{speeches}
\title{Committee Speeches from the Science and ICT Committee (22nd Assembly)}
\format{
A data frame with 15,843 rows and 9 variables:
\describe{
\item{assembly}{Assembly number (22)}
\item{date}{Date of the committee meeting}
\item{committee}{Committee name in Korean}
\item{speaker}{Speaker label as it appears in the minutes (may include
titles)}
\item{role}{Speaker role: "legislator", "chair", "minister",
"vice_minister", "senior_bureaucrat", "agency_head", "witness",
"expert_witness", "nominee", "minister_nominee", "testifier",
"public_corp_head", "broadcasting", "committee_staff"}
\item{speaker_name}{Cleaned speaker name with titles removed}
\item{member_id}{Legislator identifier (MONA_CD, links to
\code{legislators$member_id}). Available for all rows; however,
non-legislator speakers (ministers, witnesses, etc.) will not match
entries in \code{legislators}.}
\item{speech_order}{Order of the speech turn within the meeting}
\item{speech}{Full text of the speech in Korean}
}
}
\source{
National Assembly committee minutes via the
Open National Assembly Information API.
}
\usage{
speeches
}
\description{
Full corpus of 15,843 speech records from the Science, Technology,
Information, Broadcasting and Communications Committee of the 22nd
Korean National Assembly (2024). Standing committee meetings only.
}
\details{
This dataset contains the complete standing committee speech records
(no sampling) for the Science and ICT Committee of the 22nd assembly
(June-December 2024). Speeches shorter than 50 characters were excluded.

The \code{role} variable distinguishes legislators from government
officials, witnesses, and other participants. Filter to
\code{role == "legislator"} for MP speeches only, or compare how
legislators and ministers discuss the same agenda items.

This committee covers AI, telecommunications, broadcasting, space
policy, and R&D governance, making it suitable for keyword analysis,
topic modeling, and other text analysis exercises.
}
\examples{
data(speeches)

# Distribution of speech lengths
hist(nchar(speeches$speech), breaks = 100,
     main = "Speech Length Distribution", xlab = "Characters")

# Speaker roles
table(speeches$role)

# Most frequent legislator speakers
leg <- speeches[speeches$role == "legislator", ]
head(sort(table(leg$speaker_name), decreasing = TRUE), 10)

# Simple keyword search (example: AI-related speeches)
ai <- speeches[grepl("AI", speeches$speech), ]
nrow(ai)
}
\keyword{datasets}
