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Hyper-parameter optimization: towards practical sentiment analysis using a Convolutional Neural Network (CNN)

Author Affiliations

  • 1Kirinyaga University, Kerugoya, Kenya

Res. J. Computer & IT Sci., Volume 7, Issue (2), Pages 1-5, December,20 (2019)


Current advancements of in Deep Neural Networks (DNNs) has made it possible to assess the latent and sentimental polarity in text. This is helpful in many applications such as hate speech recognition and mood determination. Sentiment analysis, as it is popularly known in machine learning, is a typical task in text classification – an active research area of Natural Language Processing (NLP). One of the main challenges in machine learning is the ability to create accurate models applicable to real-life problems. In this paper, we report on the experimental results of hyper-parameter optimization of a Convolution Neural Network (CNN) – one of the most promising deep machine learning architecture for text processing as an attempt to move towards practical sentiment analysis models. The results indicate remarkable improvements over ordinary CNNs on typical machine learning datasets.


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