Published: September 24, 2019
Citation: Computer (IEEE Computer) vol. 52, no. 10, (October 2019) pp. 76-82
Author(s)
Apostol Vassilev (NIST)
I present a computationally efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results show the advantages of the new approach. Applications to security validation programs are discussed.
I present a computationally efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results show the advantages of the new approach. Applications to security...
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I present a computationally efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results show the advantages of the new approach. Applications to security validation programs are discussed.
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Keywords
deep learning; sentiment analysis; Natural Language Processing
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