Sentiment analysis for the social media: A case study for Turkish general elections

  • Yazar/lar UYSAL, Elif
    YUMUŞAK, Semih
    ÖZTOPRAK, Kasım
    DOĞDU, Erdoğan
  • Yayın Türü Konferans Bildirisi
  • Yayın Tarihi 2017
  • DOI Numarası 10.1145/3077286.3077569
  • Yayıncı Association for Computing Machinery, Inc
  • Tek Biçim Adres http://hdl.handle.net/20.500.12498/2962

The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specic keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results. © 2017 ACM.

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Eser Adı
(dc.title)
Sentiment analysis for the social media: A case study for Turkish general elections
Yayın Türü
(dc.type)
Konferans Bildirisi
Yazar/lar
(dc.contributor.author)
UYSAL, Elif
Yazar/lar
(dc.contributor.author)
YUMUŞAK, Semih
Yazar/lar
(dc.contributor.author)
ÖZTOPRAK, Kasım
Yazar/lar
(dc.contributor.author)
DOĞDU, Erdoğan
DOI Numarası
(dc.identifier.doi)
10.1145/3077286.3077569
Atıf Dizini
(dc.source.database)
Scopus
Yayıncı
(dc.publisher)
Association for Computing Machinery, Inc
Yayın Tarihi
(dc.date.issued)
2017
Kayıt Giriş Tarihi
(dc.date.accessioned)
2020-08-07T12:55:36Z
Açık Erişim tarihi
(dc.date.available)
2020-08-07T12:55:36Z
Kaynak
(dc.source)
2017 ACM SouthEast Regional Conference, ACMSE 2017
ISSN
(dc.identifier.issn)
9781450350242 (ISBN)
Özet
(dc.description.abstract)
The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specic keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results. © 2017 ACM.
Yayın Dili
(dc.language.iso)
eng
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/2962
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6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

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