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    Emerging and Vanishing Association Pattern Mining in Hydroclimatic Datasets
    (2018) Çelik, Mete; Dokuz, Ahmet Şakir; Çelik, Filiz Dadaşer
    Emerging and vanishing association patterns can be defined as association patterns whose frequencies (supports) get strongerand weaker over time, respectively. Discovering these patterns is important for several application domains such as financial andcommunication services, public health, and hydroclimatic studies. Classical association pattern mining algorithms do not considerhow the strengths of association patterns change over time. An association pattern can be defined as an emerging or vanishing patternwhen its support measure changes over time. In this paper, we focus on discovery of time evolving association patterns (i.e., emergingand vanishing association patterns) from datasets. To discover such patterns, a novel algorithm, named as Emerging and VanishingAssociation Pattern Miner (EVAPMiner) algorithm, was proposed. The proposed algorithm was evaluated using hydroclimatic datasetof Turkey. The analyses showed that the proposed algorithm successfully detects emerging and vanishing association patterns inhydroclimatic datasets.
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    Temporal Sentiment Analysis of Socially Important Locations of Social Media Users
    (Springer Science and Business Media Deutschland GmbH, 2021) Ecemiş, Alper; Dokuz, Ahmet Şakir; Celik, Mete
    Socially important locations are the places which are frequently visited by social media users. Temporal sentiment analysis of socially important locations is the process of interpretation and classification of emotions within their sharings in their socially important locations over time. Observing the temporal sentiment changes in these locations helps both to examine the emotion change in the locations and to understand the thoughts of the social media users in these locations. In this paper, Twitter is selected as social media data source and temporal sentiment analysis of socially important locations of social media users are analyzed in different time frames. For the analysis, a method, called Temporal Sentiment Analysis of Socially Important Locations (TS-SIL), is proposed in this study. In this method, first of all, socially important locations are discovered from the collected Twitter dataset. Then, sentiment analysis is performed using a dictionary based approach and several machine learning algorithms. Finally, the sharings in the locations are listed and the sentiments at these locations are analyzed by daily, weekly, and monthly basis. As a result, socially important locations of the city of Istanbul are discovered and temporal sentiment analysis of these locations are performed. Results shows that all of the socially important locations of İstanbul, except Beşiktaş Fish Market, showed emotional fluctuations over the time. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.