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Aspect detection, big data, deep learning, latent semantic indexing, online learning, regularization, topic modeling
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.
Ajeet Ram Pathak, Manjusha Pandey, Siddharth Rautaray, "Adaptive Model for Dynamic and Temporal Topic Modeling from Big Data using Deep Learning Architecture", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.6, pp.13-27, 2019. DOI:10.5815/ijisa.2019.06.02
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