In this article the description of an algorithm of a statement sentiment evaluation is done for the users' of social media language. We underline that statements of the natural language can be contradictory, emotionally complicated, ambiguous. It is the additional research task to detect the adequate formal criterion of the natural language statements ranging on t he scale “posit ive - negative”. In the article the original decision of this problem on the base of the theory of the semantic field is described. The technique was tested in the All-Russian Scientific Research Institute of Labor of the Ministry of Labor and Social Protection of the Russian Federation for investigation of population opinion to the new forms of employment. Empirical base is more than 100 000 messages of users of thematical groups in VKontakte. Analysis with the accent on the parameters: subject of tonality, object of tonality, message tonality was done. The technique of research assumes the detection of words-markers that indicate the general message tonality.
Translated title of the contributionТеория семантического поля в сентимент-анализе специфики языка пользователей различных групп социальных медиа (на примере групп фрилансеров)
Original languageEnglish
Title of host publication25th Conference of Open Innovations Association FRUCT, FRUCT 2019
EditorsValttery Niemi , Tatiana Tyutina
Place of PublicationСША
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages204-210
Number of pages7
ISBN (Electronic)978-952692440-3
ISBN (Print)978-1-7281-2786-6
StatePublished - Nov 2019
Event25th Conference of Open Innovations Association FRUCT, FRUCT 2019 - Helsinki, Finland
Duration: 5 Nov 20198 Nov 2019

Conference

Conference25th Conference of Open Innovations Association FRUCT, FRUCT 2019
Country/TerritoryFinland
CityHelsinki
Period5/11/198/11/19

    Research areas

  • natural language processing, social networking (online), social sciences computing, text analysis

    Scopus subject areas

  • Social Sciences(all)
  • Computer Science(all)

ID: 52294817