Daily and hourly mood pattern discovery of Turkish twitter users

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Mete Celik
Ahmet Sakir Dokuz


Massive amount of data-related applications and widespread usage of web technologies has started big data era. Social media data is one of the big data sources. Mining social media data provides useful insights for companies and organizations for developing their services, products or organizations. This study aims to analyze Turkish Twitter users based on daily and hourly social media sharings. By this way, daily and hourly mood patterns of Turkish social media users could be revealed in positive or negative manner. For this purpose, Support Vector Machines (SVM) classification algorithm and Term Frequency – Inverse Document Frequency (TF-IDF) feature selection technique was used. As far as our knowledge, this is the first attempt to analyze people’s all sharings on social media and generate results for temporal-based indicators like macro and micro levels.


Keywords: big data, social media, text classification, svm, tf-idf term weighting, daily and hourly mood patterns.


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How to Cite
Celik, M., & Dokuz, A. S. (2015). Daily and hourly mood pattern discovery of Turkish twitter users. Global Journal of Computer Sciences: Theory and Research, 5(2), 90-98. https://doi.org/10.18844/gjcs.v5i2.183


[1]Nguyen, T., Phung, D., Adams, B., & Venkatesh, S., (2014). Mood sensing from social media texts and its applications, Knowl Inf Syst, 39(3), 667-702.

[2]Turney, P.D., (2002). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, 417-424

[3]O'Connor, B., Balasubramanyan, R., Routledge, B.R., & Smith, N.A., (2010). From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 122-129

[4]Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M., (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 178-185

[5]Vural, A.G., Cambazoglu, B.B., Senkul, P., & Tokgoz, Z.O., (2013). A Framework for Sentiment Analysis in Turkish: Application to Polarity Detection of Movie Reviews in Turkish, in Computer and Information Sciences III, Springer London, 437-445

[6]Gezici, G., Yanikoglu, B., Tapucu, D., & Saygın, Y., (2012). New Features for Sentiment Analysis: Do Sentences Matter? SDAD 2012 The 1st International Workshop on Sentiment Discovery from Affective Data,. 5-15

[7]Pang, B., Lee, L., & Vaithyanathan, S., (2002). Thumbs up?: sentiment classification using machine learning techniques, Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, 2002, pp. 79-86

[8]Go, A., Bhayani, R., & Huang, L., (2009). Twitter sentiment classification using distant supervision, CS224N Project Report, Stanford, 1, 1-12.

[9]Bifet, A., & Frank, E., (2010). Sentiment Knowledge Discovery in Twitter Streaming Data, in Discovery Science, Springer Berlin Heidelberg, 1-15

[10]Jiang, L., Yu, M., Zhou, M., Liu, X., and Zhao, T., (2011). Target-dependent Twitter sentiment classification, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1, 151-160

[11]Erogul, U., (2009). Sentiment Analysis in Turkish, METU, Ankara.

[12]Tantug, A.C., (2010). Document Categorization with Modified Statistical Language Models for Agglutinative Languages, International Journal of Computational Intelligence Systems, 3(5), 632-645.

[13] Received November 02, 2015 from: https://dev.twitter.com/

[14]Read, J., (2005). Using emoticons to reduce dependency in machine learning techniques for sentiment classification, Proceedings of the ACL Student Research Workshop, 43-48

[15]Sparck Jones, K., (1972). A statistical in terpretation of term specificity and its application in retrieval, Journal of Documentation, 28(1), 11-21.

[16]Boser, B.E., Guyon, I.M., & Vapnik, V.N., (1992). A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, 144-152