NDLI: SLT-Based ELM for Big Social Data Analysis

Content Provider	Springer Nature Link
Author	Oneto, Luca Bisio, Federica Cambria, Erik Anguita, Davide
Copyright Year	2016
Abstract	Recently, social networks and other forms of media communication have been gathering the interest of both the scientific and the business world, leading to the increasing development of the science of opinion and sentiment analysis. Facing the huge amount of information present on the Web represents a crucial task and leads to the study and creation of efficient models able to tackle the task. To this end, current research proposes an efficient approach to support emotion recognition and polarity detection in natural language text. In this paper, we show how the most recent advances in statistical learning theory (SLT) can support the development of an efficient extreme learning machine (ELM) and the assessment of the resultant model’s performance when applied to big social data analysis. ELM, developed to overcome some issues in back-propagation networks, represents a powerful learning tool. However, the main problem is represented by the necessity to cope with a large number of available samples, and the generalization performance has to be carefully assessed. For this reason, we propose an ELM implementation that exploits the Spark distributed in memory technology and show how to take advantage of SLT results in order to select ELM hyperparameters able to provide the best generalization performance.
Starting Page	259
Ending Page	274
Page Count	16
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	9
Issue Number	2
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-11-26
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sentiment analysis Big data Extreme learning machines Model selection Spark distributed in memory computing Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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