Mapreduce and semantics enabled event detection using social media

• Autorzy: Yan P.

Identyfikatory

DOI

10.1515/jaiscr-2017-0014

• Języki publikacji: EN

Abstrakty

EN

Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.

Słowa kluczowe

EN

event detection; social media; semantic relatedness; MapReduce

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2017
• Tom: Vol. 7, No. 3
• Strony: 201--213
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Yan P.

• 3M Corporate Research Laboratory 3M Corporate Headquarters3M Center, St. Paul, MN 55144

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).