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Title |
Sentiment Orientation Using Deep Learning Sequential and Bidirectional Models |
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Author |
Hasan J. Alyamani |
| Citation |
Vol. 21 No. 11 pp. 23-30 |
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Abstract |
Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, un-supervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size. |
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Keywords |
Deep Learning, LSTM, CNN, Machine Learning, Supervised and Un-Supervised Learning |
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