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Unified Framework For Deep Learning Based Text Classification
[Full Text]
AUTHOR(S)
Dr Sheelesh Kumar Sharma, Navel Kishor Sharma, Dr Gajendra Singh
KEYWORDS
Text classification, deep learning, text classification approaches, LSTM, RNN, NLP, framework for classification, applications of deep learning
ABSTRACT
Deep learning has emerged as a very popular approach for solving large scale pattern recognition problems. In recent times, it has solved various text mining problems with improved accuracy as compared to pre-existing approaches. There are deep learning based AI systems that have been trained to do sentiment analysis on social media or business data, opinion mining, text document classification & clustering etc. The models for deep learning for text classification include convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory (LSTM) networks, deep belief networks (DBN), fusion approaches etc. This paper presents a unified framework for deep learning based text classification. The framework has 6 layers segmenting basic components of a typical classification system. Here, the review of state-of-the-art deep learning based text classification methods and their applications in different domains has been presented. Moreover, the paper also discusses the limitations of deep learning and throws light on various challenges that may turn to the future research directions in the field. Paper presents a comparative study of different deep learning based text classification approaches based on parameters like approach used, data sets and accuracy.
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