NDLI: Neural Network-Based Architecture for Sentiment Analysis in Indian Languages

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Neural Network-Based Architecture for Sentiment Analysis in Indian Languages

Content Provider	Scilit
Author	Bhargava, Rupal Arora, Shivangi Sharma, Yashvardhan
Copyright Year	2018
Abstract	Sentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages (Hindi, Bengali, and Tamil). Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks (RNNs), long short-term memory (LSTM), convolutional neural network (CNN)] with optimum parameter settings (to avoid over-fitting and error accumulation). These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect the overall performance of the approach. A comparison has also been performed with existing approaches to find out if neural networks have an added advantage over traditional machine learning techniques.
Related Links	http://www.degruyter.com/downloadpdf/j/jisys.ahead-of-print/jisys-2017-0398/jisys-2017-0398.xml
Ending Page	375
Page Count	15
Starting Page	361
ISSN	03341860
e-ISSN	2191026X
DOI	10.1515/jisys-2017-0398
Journal	Journal of Intelligent Systems
Issue Number	3
Volume Number	28
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2018-06-05
Access Restriction	Open
Subject Keyword	Journal of Intelligent Systems Cybernetical Science Sentiment Analysis Deep Learning Indian Languages Convolutional Neural Network (cnn) Recurrent Neural Network (rnn) Long Short-term Memory (lstm) Journal: Journal of Intelligent Systems, Vol- 28
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Software

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