Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

Ozturk N., Ayvaz S.

TELEMATICS AND INFORMATICS, cilt.35, sa.1, ss.136-147, 2018 (SSCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Konu: 1
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.tele.2017.10.006
  • Sayfa Sayıları: ss.136-147


The use of social media has become an integral part of daily routine in modern society. Social media portals offer powerful public platforms where people can freely share their opinions and feelings about various topics with large crowds. In the current study, we investigated the public opinions and sentiments towards the Syrian refugee crisis, which has affected millions of people and has become a widely discussed, polarizing topic in social media around the world. To analyze public sentiments about the topic on Twitter, we collected a total of 2381,297 relevant tweets in two languages including Turkish and English. Turkish sentiments were considered important as Turkey has welcomed the largest number of Syrian refugees and Turkish tweets carried information to reflect public perception of a refugee hosting country first handedly. We performed a comparative sentiment analysis of retrieved tweets. The results indicated that the sentiments in Turkish tweets were significantly different from the sentiments in English tweets. We found that Turkish tweets carried slightly more positive sentiments towards Syrians and refugees than neutral and negative sentiments, nevertheless the sentiments of tweets were almost evenly distributed among the three major categories. On the other hand, the largest number of English tweets by a significant margin contained neutral sentiments, which was followed by the negative sentiments. In comparison to the ratio of positive sentiments in Turkish tweets, 35% of all Turkish tweets, the proportion of English tweets contained remarkably less positive sentiments towards Syrians and refugees, only 12% of all English tweets.