A Robust Approach to Use LSTM-RNN in Natural Language Processing

Abstract

The integration of LSTM-RNN networks into natural language processing (NLP) has shown promising potential for capturing spatial and temporal dependencies in sequential data. In contrast to conventional LSTM models, which focus primarily on temporal dynamics, LSTMRNN incorporates convolutional operations to better capture local patterns and hierarchical structures in text. This hybrid architecture is particularly well suited for tasks where sequential
and spatial relationships need to be modeled, such as machine translation, sentiment analysis, and text generation. Therefore, a robust approach using LSTM-RNN for natural language processing is proposed in this paper. This study proposes the application of LSTM-RNN for sentiment analysis and utilizes its ability to capture semantic and syntactic relations in texts. The model processes input sequences through an embedding layer to represent words as dense vectors, followed by one or more LSTM layers with dropout regularization to prevent overfitting. The final dense layer with a sigmoid or softmax activation function predicts sentiment polarity.