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Ako, A.

This study presents an approach to extracting data from amazon dataset and performing some preprocessing on it by combining the techniques of Bi-Directional Long Short-Term Memory and 1-Dimensional Convolution Neural Network to classify the opinions into targets. After parsing the dataset and identi...

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Published: 2019-09
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LEADER 00000njm a2000000a 4500
001 oai:repository.ui.edu.ng:123456789/11368
042 |a dc 
720 |a Ojo, A. K.  |e author 
260 |c 2019-09 
520 |a This study presents an approach to extracting data from amazon dataset and performing some preprocessing on it by combining the techniques of Bi-Directional Long Short-Term Memory and 1-Dimensional Convolution Neural Network to classify the opinions into targets. After parsing the dataset and identifying desired information, we did some data gathering and preprocessing tasks. The feature selection technique was developed to extract structural features which refer to the content of the review (Parts of Speech Tagging) along with extraction of behavioral features which refer to the meta-data of the review. Both behavioral and structural features of reviews and their targets were extracted. Based on extracted features, a vector was created for each entity which consists of those features. In evaluation phase, these feature vectors were used as inputs of classifier to identify whether they were fake or non-fake entities. It could be seen that the proposed solution has over 90% of the predictions when compared with other work which had 77%. This increase was as a result of the combination of the bidirectional long short-term memory and the convolutional neural network algorithms. 
024 8 |a 1110-2586 
024 8 |a ui_art_ojo_detection_2019 
024 8 |a Egyptian Computer Science Journal 43(3), pp. 103-114 
024 8 |a https://repository.ui.edu.ng/handle/123456789/11368 
653 |a Fake reviews detection 
653 |a Opinion Spam 
653 |a Behavioral features 
653 |a Convolution Neural Network 
653 |a Bi-Directional Long Short-Term Memory 
245 0 0 |a Ako, A.