Adaptive Model for Dynamic and Temporal Topic Modeling from Big Data using Deep Learning Architecture

Due to freedom to express views, opinions, news, etc and easier method to disseminate the information to large population worldwide, social media platforms are inundated with big streaming data characterized by both short text and long normal text. Getting the glimpse of ongoing events happening ove...

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Bibliographic Details
Published in:International journal of intelligent systems and applications Vol. 11; no. 6; pp. 13 - 27
Main Authors: Pathak, Ajeet Ram, Pandey, Manjusha, Rautaray, Siddharth
Format: Journal Article
Language:English
Published: Hong Kong Modern Education and Computer Science Press 08.06.2019
Subjects:
Big Data
Data management
Data mining
Data transmission
Deep learning
Digital media
Machine learning
Modelling
Regularization
Social networks
Trends
ISSN:2074-904X, 2074-9058
Online Access:Get full text
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Summary:Due to freedom to express views, opinions, news, etc and easier method to disseminate the information to large population worldwide, social media platforms are inundated with big streaming data characterized by both short text and long normal text. Getting the glimpse of ongoing events happening over social media is quintessential from the viewpoint of understanding the trends, and for this, topic modeling is the most important step. With reference to increase in proliferation of big data streaming from social media platforms, it is crucial to perform large scale topic modeling to extract the topics dynamically in an online manner. This paper proposes an adaptive framework for dynamic topic modeling from big data using deep learning approach. Approach based on approximation of online latent semantic indexing constrained by regularization has been put forth. The model is designed using deep network of feed forward layers. The framework works in an adaptive manner in the sense that model is extracts incrementally according to streaming data and retrieves dynamic topics. In order to get the trends and evolution of topics, the framework supports temporal topic modeling, and enables to detect implicit and explicit aspects from sentences also.
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ISSN:2074-904X
2074-9058
DOI:10.5815/ijisa.2019.06.02