You are here

Home » Content

TÜRKÇE TWEETLERDE KULLANICI GÖRÜŞ ANALİZİ

USER OPINION ANALYSIS ON TURKISH TWEETS

Journal Name:

  • Mugla Journal of Science and Technology

Publication Year:

  • 2017
DOI: 
10.22531/muglajsci.286291

Key Words:

Keywords (Original Language):

Author NameUniversity of Author
Abstract (2. Language): 
Nowadays, the use of micro-blogging services such as Twitter seems to increase exponentially with the popularity of social media. Through these services, users share their opinions, complaints, requests and suggestions about the subjects or institutions and organizations they wish. In this study, it is aimed to develop a system which can detect general opinion s and determine what they are by classifying and summarizing the Turkish tweets sent by users via Twitter. At classification step of this system, SVM and Naive Bayes were used together. The Hybrid TF-IDF method was preferred for the purpose of summarizing classified tweets. According to the results obtained, this developed system has been found to be able to successfully determine the opinions of the users and to get a general idea of what is going on.
Bookmark/Search this post with
Abstract (Original Language): 
Günümüzde sosyal medyanın popülerlik kazanmasıyla birlikte, Twitter gibi mikro-blog servislerinin kullanımının katlanarak arttığı görülmektedir. Bu servisler aracılığı ile kullanıcılar, diledikleri konu veya kurum ve kuruluşlar hakkında görüşlerini, şikâyetlerini, istek ve önerilerini paylaşmaktadır. Bu çalışmada, Twitter üzerinden kullanıcıların gönderdikleri Türkçe tweetlerin sınıflandırılmasını ve her bir sınıfa ait tweetlerin özetlenmesini otomatik hale getirip raporlayan sistem geliştirilerek, genel görüşün tespiti ve neler olduğunun belirlenmesi hedeflenmiştir. Tweetlerin sınıflandırılması için SVM ve Naïve Bayes yöntemleri birlikte kullanılmıştır. Sınıflandırılan tweetlerin özetlenmesi amacı ile Hibrit TF-IDF yöntemi tercih edilmiştir. Elde edilen sonuçlara göre, geliştirilen bu sistemin, kullanıcı görüşlerinin tespiti ve neler olduğu hakkında genel fikir edinmeye başarılı bir şekilde olanak sağladığı görülmüştür.
FULL TEXT (PDF): 
PDF icon arastirmax-turkce-tweetlerde-kullanici-gorus-analizi.pdf
  • 1
35
38
  • Turkish

REFERENCES

References: 

[1] Java, A.,“Why we Twitter: Understanding microblogging usage and communities”, Proceeding of the 9th Web KDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, ACM, 2007.
[2] Niharika, S.,Latha, V.S. and Lavany, D.R., “A Survey On Text Categorization”, International Journal of Computer Trends and Technology- Volume 3, Issue1, 2012.
[3] Neethu, M.S. and Rajasree, R., “Sentiment Analysis in Twitter using Machine Learning Techniques”, 4th ICCNT 2013 July 4-6, Tiruchengode, India, 2013.
[4] Bahrainian, S.A. and Dengel, A., “Sentiment Analysis Using Sentiment Feautures”,IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), 2013.
[5] Lima, C.E.S. and De Castro, L.N., “Automatic Sentiment Analysis of Twitter Messages”, Fourth International Conference on Computational Aspects Of Social Networks, 2012.
[6] Bahrainian, S.A. and Dengel, A., “Sentiment Analysis and Summarization of Twitter Data“,Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering, p. 227-234, 2013.
[7] Sharifi, B.,Hutton, M.A. and Kalita, J.K., “Experiments in Microblog Summarization”, In Proc. of IEEE Second International Conference on Social Computing ,2010.
[8] Inouye, D. and Jugal, K.,“Comparing Twitter Summarization Algorithms for Multiple Post Summaries”, IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing, 2011.
[9] Kebabcı, K. and Karslıgil, M.E., “High priority tweet detection and summarization in natural disasters”, 23nd Signal Processing and Communications Applications Conference (SIU), p. 1280 – 1283, 2015.

Thank you for copying data from http://www.arastirmax.com