GENETICALLY OPTIMIZED GAIN RATIO FEED FORWARD NEURAL NETWORK ALGORITHM FOR REVIEW OPINION CLASSIFICATION
DOI:
https://doi.org/10.61841/kv6dcw06Keywords:
Genetic Algorithm, Classification, Machine Learning, Neural Network, Opinion MiningAbstract
Opinion mining and sentiment analysis have become significant and vital areas of web content mining and text mining, and many researchers have paid attention over the past decade. This research paper identified the importance of the genetic optimization technique and used it to optimize the neural network classifier algorithm Gain Ratio Feedforward Neural Network algorithm (GR_FFNN). The proposed Genetically Optimized GR_FFNNs algorithm is used to optimize the vital parameter momentum and learning rate by using one of the leading soft computing approaches, Genetic Optimization algorithms. The genetically optimized GR-FFNN algorithm was investigated with the mobile learning app reviews. It classifies the reviews into three different classes based on the opinions extracted along with their polarity. The methodology, architecture, and results of the proposed Genetically Optimized Gain Ratio FeedForward Neural Network (Genetically Optimized GR-FFNN) algorithm are discussed elaborately.
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