Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

A social media big data mining framework for detecting sentiments in multiple languages

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dc.contributor Ph.D. Program in Management Information Systems.
dc.contributor.advisor Özturan, Meltem.
dc.contributor.author Coşkun, Mustafa.
dc.date.accessioned 2023-03-16T12:53:11Z
dc.date.available 2023-03-16T12:53:11Z
dc.date.issued 2018.
dc.identifier.other MIS 2018 C77 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/18209
dc.description.abstract The popularity of social media platforms has generated a new social interaction environment thus a new collaboration network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments. One of these platforms is Twitter, which provides researchers data potential of benefit for their studies. Based on Twitter data, in this study a multilingual sentiment detection framework is proposed to compute European Gross National Happiness (GNH). This framework consists of a novel data collection, filtering and sampling method, and multilingual sentiment detection algorithm for social media big data, and tested with nine European countries (United Kingdom, Germany, Sweden, Turkey, Portugal, Netherlands, Italy, France and Spain) and their national languages over six-year period. The reliability of the data is checked with peak/troughs comparison for special days from Wikipedia. The validity is checked with a group of correlation analyses with OECD Life Satisfaction survey reports’, currency exchanges, and national stock market time series data. Then, the European GNH map is drawn for six years. Lastly, an exploratory study for determining the relationships between users’ Twitter account features (number of tweets, number of followers etc.) and happiness polarities are analyzed. Main aim of this study is to propose a novel multilingual social media sentiment analysis framework for calculating GNH for countries and change the way of OECD type organizations’ survey and interview methodology. Also, it is believed that this framework can serve more detailed results (e.g. daily or hourly sentiments of society in different languages).
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2018.
dc.subject.lcsh Big data -- Social aspects.
dc.subject.lcsh Database management.
dc.subject.lcsh Information storage and retrieval systems.
dc.subject.lcsh Multimedia systems.
dc.title A social media big data mining framework for detecting sentiments in multiple languages
dc.format.pages xvi, 104 leaves ;


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