Purpose-Neural network(NN)-based deep learning(DL)approach is considered for sentiment analysis(SA)by incorporating convolutional neural network(CNN),bi-directional long short-term memory(Bi-LSTM)and attention methods...Purpose-Neural network(NN)-based deep learning(DL)approach is considered for sentiment analysis(SA)by incorporating convolutional neural network(CNN),bi-directional long short-term memory(Bi-LSTM)and attention methods.Unlike the conventional supervised machine learning natural language processing algorithms,the authors have used unsupervised deep learning algorithms.Design/methodology/approach-The method presented for sentiment analysis is designed using CNN,Bi-LSTM and the attention mechanism.Word2vec word embedding is used for natural language processing(NLP).The discussed approach is designed for sentence-level SA which consists of one embedding layer,two convolutional layers with max-pooling,oneLSTMlayer and two fully connected(FC)layers.Overall the system training time is 30 min.Findings-The method performance is analyzed using metrics like precision,recall,F1 score,and accuracy.CNN is helped to reduce the complexity and Bi-LSTM is helped to process the long sequence input text.Originality/value-The attention mechanism is adopted to decide the significance of every hidden state and give a weighted sum of all the features fed as input.展开更多
文摘Purpose-Neural network(NN)-based deep learning(DL)approach is considered for sentiment analysis(SA)by incorporating convolutional neural network(CNN),bi-directional long short-term memory(Bi-LSTM)and attention methods.Unlike the conventional supervised machine learning natural language processing algorithms,the authors have used unsupervised deep learning algorithms.Design/methodology/approach-The method presented for sentiment analysis is designed using CNN,Bi-LSTM and the attention mechanism.Word2vec word embedding is used for natural language processing(NLP).The discussed approach is designed for sentence-level SA which consists of one embedding layer,two convolutional layers with max-pooling,oneLSTMlayer and two fully connected(FC)layers.Overall the system training time is 30 min.Findings-The method performance is analyzed using metrics like precision,recall,F1 score,and accuracy.CNN is helped to reduce the complexity and Bi-LSTM is helped to process the long sequence input text.Originality/value-The attention mechanism is adopted to decide the significance of every hidden state and give a weighted sum of all the features fed as input.
