Daily and hourly mood pattern discovery of Turkish twitter users

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Published: Dec 31, 2015
Updated: 2015-12-31
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2015-12-31 (1)
DOI: https://doi.org/10.18844/gjcs.v5i2.183

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Mete Celik
Engineering Faculty, Computer Engineering Department, Erciyes University, Kayseri 38350, Turkey.
Ahmet Sakir Dokuz
Engineering Faculty, Computer Engineering Department, Erciyes University, Kayseri 38350, Turkey.

Abstract

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
Issue
Vol. 5 No. 2 (2015): November
Section
Articles

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