GENETICALLY OPTIMIZED GAIN RATIO FEED FORWARD NEURAL NETWORK ALGORITHM FOR REVIEW OPINION CLASSIFICATION

1Dr. Helen Josephine V L, Dr. V.S Prakash, Dr. Aruna Devi M

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Abstract:

The opinion mining and sentiment analysis has become significant and vital area of web content mining and text mining and many researchers paying attention over the past decade . This research paper identified the importance Genetic optimization technique and used to optimize the neural network classifier algorithm Gain Ratio Feed forward Neural Network algorithm (GR_FFNN). The proposed Genetically Optimized GR_FFNNs algorithm used to optimize the vital parameter momentum and learning rate by using one of the leading soft computing approaches Genetic Optimization algorithms. Genetically optimized GR-FFNN algorithm was investigated with the mobile learning app reviews. It classifies the reviews into three different class based the opinion extracted along with their polarity. The methodology, architecture and the results of the proposed Genetically Optimization Gain Ratio FeedForward Neural Network (Genetically Optimized GR-FFNN) algorithm are discussed elaborately.

Keywords:

Genetic Algorithm, Classification, Machine Learning, Neural Network, Opinion Mining

Paper Details
Month2
Year2020
Volume24
IssueIssue 2
Pages4429-4441