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Neural Network-Based Architecture for Sentiment Analysis in Indian Languages by Rupal Bhargava; Shivangi Arora; Yashvardhan Sharma is a Computer Science article available to read on EtoBox.

What is Neural Network-Based Architecture for Sentiment Analysis in Indian Languages about?

## 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.

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Author

Rupal Bhargava; Shivangi Arora; Yashvardhan Sharma

Publisher

Walter de Gruyter GmbH

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)