title: HOW TO USE QUANTITATIVE ANALYSIS AND TEXT MINING METHODS TO ANALYZE POLITICAL TALK SHOWS
metadata.name: dc.contributor.author
metadata.value: Donina, Olga V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Olefir, Nadezhda A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-10-24T12:57:23Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-10-24T12:57:23Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024
metadata.lang: -
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/2782-1943.2023.12
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/47816
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The article presents a linguistic analysis of the socio-political talk show ‘Time
  will Tell’. The significance of this work is attributed to the scarcity of similar research
  and the growing prominence of political talk shows on television over
  the past year. As material for the analysis, we used the scripts of the broadcasts
  (which are in the public domain), obtained through automatic transcription
  of videos from YouTube and after manual editing of these texts, which
  was necessary due to the disadvantages of the automated process: the lack of
  punctuation marks and the marking of the replicas belonging to one or another
  speaker. From the archive of the programme ‘Time will Tell’, we selected
  14 days of the year 2022. The main criterion for selection was the number of
  releases published each day. The typical number is 3 or 2 releases per day, but
  the programme was released 4 to 6 times over 14 days. In total, 64 hours of
  the TV programme were processed and scripts were obtained. In the research
  we used corpus methods as well as quantitative methods of linguistic analysis.
  As a result, we obtained a general list of words consisting of 242,560 word usages,
  which were analysed using different text mining methods. The 100 most
  frequent lexical items were classified into semantic groups and analysed
  quantitatively. We conducted sentiment analysis and topic modeling of these
  lemmas. In addition, we identified lexical innovations found in the talk show
  hosts’ speech, which are not present in the Multimedia subcorpus of the Russian
  National Corpus, and analysed their bi- and trigrams.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Synergy of Languages & Cultures 2023: Interdiscipilinary Studies;
metadata.lang: -
metadata.name: dc.subject
metadata.value: socio-political talk shows
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: quantitative analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: text mining
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: sentiment analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: topic modeling
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: N-grams
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: HOW TO USE QUANTITATIVE ANALYSIS AND TEXT MINING METHODS TO ANALYZE POLITICAL TALK SHOWS
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 146-154.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/47816/1/146-154.pdf
resources.format: Adobe PDF
resources.size: 2,4 MB