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SOSYAL MEDYA ÜZERİNDE VERİ ANALİZİ: TWITTER

Yıl 2017, Cilt: 22 Sayı: Kayfor 15 Özel Sayısı, 1991 - 1998, 30.12.2017

Mehmet Albayrak Kamil Topal Volkan Altıntaş

Öz

Gelişen teknoloji gün geçtikçe yeni olanaklar sunmaktadır. Sosyal Medya bu gelişmelerin en önemli sonuçlarından biri olmuştur. Sosyal medyayla birlikte, iletişim tek taraflı boyuttan, daha interaktif bir yöne evrilmiş olup, haberlere olan tepkilerin nabzını tutmak son yıllardaki önemli araştırma konularından birisi haline gelmiştir. Bu çalışmada sosyal medyada tartışılan sosyal bir konunun verileri toplanmış ve duygu analizleri yapılmıştır. Ayrıca bulunan sonuçlar görselleştirilmiştir. Sonuç olarak, karar alıcı mercilerin tutumunu, sosyal medyadaki tepkiler şekillendirebilecek ve kitleler sesini bu şekilde çok daha hızlı ve etkili bir şekilde duyurabileceklerdir.

Anahtar Kelimeler

Sosyal Medya Kamu Yönetimi Veri Madenciliği Metin Madenciliği Duygu Analizi

Kaynakça

  • (2017,8.1). TWEEPY:http://www. tweepy.org/ adresinden alındı
  • (2017, 8 1). NLTK 3.2.5 Kütüphanesi. http://www.nltk.org/ adresinden alındı.
  • BOLLEN, J., MAO, H. & ZENG, X.-J. (2011). Twitter mood predicts the stock market. J. Comput. Science, 2, 1-8. CHRZANOWSKI, M. & LEVICK, D. (2012). Using Twitter to Predict Voting Behavior.
  • CONOVER, M., FERRARA, E., MENCZER, F. & FLAMMINI, A. (2013). The Digital Evolution of Occupy Wall Street. PLoS ONE8.
  • DEHKHARGHANI, R., SAYGIN, Y., YANIKOGLU, B. & OFLAZER, K. (2016). SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 1-19.
  • DEHKHARGHANI, R., YANIKOGLU, B., SAYGIN, Y. & OFLAZER, K. (2017). Sentiment analysis in Turkish at different granularity levels. Natural Language Engineering, 535–559.
  • EDİZ, I. (2008). Osmanlı’dan Cumhuriyet’in İlk Yıllarına Kahvehaneler ve Sosyal Değişim. SAÜ Fen Edebiyat Dergisi, 179-189.
  • ELKİN, S. L., TOPAL, K. & BEBEK, G. (2017). Network based model of social media big data predicts contagious disease diffusion. Information Discovery and Delivery.
  • FU, X., LIU, W., XU, Y. & CUI, L. (2017). Combine HowNet lexicon to train phrase recursive autoencoder for sentence-level sentiment analysis. Neurocomputing, 18-27.
  • GANDY, L. & HEMPHILL, L. (2014). Will this Celebrity Tweet Go Viral? An Investigation of Retweets. Academy of Science and Engineering (ASE).
  • GHIASSI, M., SKINNER, J. & ZIMBRA, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 6266–6282.
  • HEALEY, C. G. (2017, 08 25). Visualizing Twitter Sentiment. https://www.csc2.ncsu.edu/faculty/healey/tweet_viz/ adresinden alındı.
  • KAYA, M., FİDAN, G., & TOROSLU, I. H. (2012). Sentiment Analysis of Turkish Political News. Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (s. 174-180). Washington, DC, USA: IEEE Computer Society.
  • KORKMAZ, A. (2012). Arap Baharı Sürecinde İnternet ve Sosyal Medyanın Rolü. International Symposium on Language and Communication: Research Trends and Challenges (ISLC). Izmir.
  • LI, J. & CARDIE, C. (2014). Timeline generation: Tracking individuals on twitter. Proceedings of the 23rd international conference on World wide web (s. 643-652). ACM.
  • MONTEJO-RÁEZ, A., MARTÍNEZ-Cámara, E., MARTÍN-VALDIVIA, M., & Urena-López, L. (2014). Ranked WordNet graph for Sentiment Polarity Classification in Twitter. Computer Speech and Language, 93-107.
  • O'CONNOR, B., BALASUBRAMANYAN, R., ROUTLEDGE, B. R., & SMİTH, N. A. (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. ICWSM.
  • OKTAY, H., FIRAT, A. & ERTEM, Z. (2014). Demographic breakdown of twitter users: An analysis based on names. Academy of Science and Engineering (ASE).
  • RUSELL, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 1161-1178.
  • SAIF, H., HE, Y., FERNANDEZ, M. & Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 5-19.
  • SINHA, S., DYER, C., GIMPEL, K. & SMITH, N. A. (2013). Predicting the NFL using Twitter. CoRR, abs/1310.6998.
  • STATISTA, T. S. (2017, 09 01). Twitter Statistics & Facts. https://www.statista.com/topics/737/twitter/ adresinden alındı.
  • Stats, I. L. (2017, 09 01). http://www.internetlivestats.com/twitter-statistics/ adresinden alındı.
  • Twitter. (2017, 08 14). http://twitter.com adresinden alındı.
  • Twitter Apps. (2017, 8 1). https://apps.twitter.com/ adresinden alındı.

Yıl 2017, Cilt: 22 Sayı: Kayfor 15 Özel Sayısı, 1991 - 1998, 30.12.2017

Mehmet Albayrak Kamil Topal Volkan Altıntaş

Öz

Recent advancing technologies provide new opportunities, such as social media, becoming one of the most crucial communication methods. Communication has changed from one-way to more interactive way, by enabling people to react the news with social media. Researchers have paid more attention to such these reactions. In this study, we aim to find an issue discussed by people over Twitter. Then, we show and visualize main results of people’s opinions and decision makers can use them to find the optimal way for the large population.

Anahtar Kelimeler

Social Media Public Administration Data Mining Text Mining Sentiment Analysis

Kaynakça

  • (2017,8.1). TWEEPY:http://www. tweepy.org/ adresinden alındı
  • (2017, 8 1). NLTK 3.2.5 Kütüphanesi. http://www.nltk.org/ adresinden alındı.
  • BOLLEN, J., MAO, H. & ZENG, X.-J. (2011). Twitter mood predicts the stock market. J. Comput. Science, 2, 1-8. CHRZANOWSKI, M. & LEVICK, D. (2012). Using Twitter to Predict Voting Behavior.
  • CONOVER, M., FERRARA, E., MENCZER, F. & FLAMMINI, A. (2013). The Digital Evolution of Occupy Wall Street. PLoS ONE8.
  • DEHKHARGHANI, R., SAYGIN, Y., YANIKOGLU, B. & OFLAZER, K. (2016). SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 1-19.
  • DEHKHARGHANI, R., YANIKOGLU, B., SAYGIN, Y. & OFLAZER, K. (2017). Sentiment analysis in Turkish at different granularity levels. Natural Language Engineering, 535–559.
  • EDİZ, I. (2008). Osmanlı’dan Cumhuriyet’in İlk Yıllarına Kahvehaneler ve Sosyal Değişim. SAÜ Fen Edebiyat Dergisi, 179-189.
  • ELKİN, S. L., TOPAL, K. & BEBEK, G. (2017). Network based model of social media big data predicts contagious disease diffusion. Information Discovery and Delivery.
  • FU, X., LIU, W., XU, Y. & CUI, L. (2017). Combine HowNet lexicon to train phrase recursive autoencoder for sentence-level sentiment analysis. Neurocomputing, 18-27.
  • GANDY, L. & HEMPHILL, L. (2014). Will this Celebrity Tweet Go Viral? An Investigation of Retweets. Academy of Science and Engineering (ASE).
  • GHIASSI, M., SKINNER, J. & ZIMBRA, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 6266–6282.
  • HEALEY, C. G. (2017, 08 25). Visualizing Twitter Sentiment. https://www.csc2.ncsu.edu/faculty/healey/tweet_viz/ adresinden alındı.
  • KAYA, M., FİDAN, G., & TOROSLU, I. H. (2012). Sentiment Analysis of Turkish Political News. Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (s. 174-180). Washington, DC, USA: IEEE Computer Society.
  • KORKMAZ, A. (2012). Arap Baharı Sürecinde İnternet ve Sosyal Medyanın Rolü. International Symposium on Language and Communication: Research Trends and Challenges (ISLC). Izmir.
  • LI, J. & CARDIE, C. (2014). Timeline generation: Tracking individuals on twitter. Proceedings of the 23rd international conference on World wide web (s. 643-652). ACM.
  • MONTEJO-RÁEZ, A., MARTÍNEZ-Cámara, E., MARTÍN-VALDIVIA, M., & Urena-López, L. (2014). Ranked WordNet graph for Sentiment Polarity Classification in Twitter. Computer Speech and Language, 93-107.
  • O'CONNOR, B., BALASUBRAMANYAN, R., ROUTLEDGE, B. R., & SMİTH, N. A. (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. ICWSM.
  • OKTAY, H., FIRAT, A. & ERTEM, Z. (2014). Demographic breakdown of twitter users: An analysis based on names. Academy of Science and Engineering (ASE).
  • RUSELL, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 1161-1178.
  • SAIF, H., HE, Y., FERNANDEZ, M. & Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 5-19.
  • SINHA, S., DYER, C., GIMPEL, K. & SMITH, N. A. (2013). Predicting the NFL using Twitter. CoRR, abs/1310.6998.
  • STATISTA, T. S. (2017, 09 01). Twitter Statistics & Facts. https://www.statista.com/topics/737/twitter/ adresinden alındı.
  • Stats, I. L. (2017, 09 01). http://www.internetlivestats.com/twitter-statistics/ adresinden alındı.
  • Twitter. (2017, 08 14). http://twitter.com adresinden alındı.
  • Twitter Apps. (2017, 8 1). https://apps.twitter.com/ adresinden alındı.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Mehmet Albayrak

Kamil Topal Bu kişi benim

Volkan Altıntaş Bu kişi benim

Yayımlanma Tarihi 30 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 22 Sayı: Kayfor 15 Özel Sayısı

Kaynak Göster

APA Albayrak, M., Topal, K., & Altıntaş, V. (2017). SOSYAL MEDYA ÜZERİNDE VERİ ANALİZİ: TWITTER. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 22(Kayfor 15 Özel Sayısı), 1991-1998.
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