A survey of deep learning techniques in the field of sentiment analysis for the hindi language

Kumar Soni Vijay, Selot Smita
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Abstract

In the domain of natural language processing, sentiment analysis is an important field for any review. Nowadays Indian languages are more popular for any product review. In North India, Hindi is the most widely used language. People having Hindi as their mother tongue can easily express their opinions and thoughts through that language. In the field of research, Hindi language has many challenges due to very less research work, limited data for analysis and less size of corpus data. Deep learning techniques are currently used for predicting feelings. Recurrent Neural Network (RNN), particularly the Long Short Term Memory (LSTM) and Convolution Neural Network (CNN), are two generally used deep learning approaches. Depending on the domain area of application, the strategies are utilized in combinations or as stand-alone procedures. This review paper emphases on the numerous flavours of deep learning approaches employed in various applications of sentiment analysis at the sentence and aspect levels.
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深度学习技术在印地语情感分析领域的研究
在自然语言处理领域,情感分析是一个重要的领域。如今,印度语言在任何产品评论中都更受欢迎。在印度北部,印地语是使用最广泛的语言。以印地语为母语的人可以很容易地通过这种语言表达自己的观点和想法。在研究领域,由于研究工作很少,可供分析的数据有限,语料库数据规模较小,印地语面临许多挑战。深度学习技术目前被用于预测情绪。递归神经网络(RNN),特别是长短期记忆(LSTM)和卷积神经网络(CNN)是两种常用的深度学习方法。根据应用领域的不同,这些策略可以组合使用,也可以单独使用。这篇综述文章强调了在句子和方面层面的情感分析的各种应用中采用的深度学习方法的许多风味。
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