Analysis of Fine Food Reviews Using Intelligent Computing Model

Automated overall review score based on text descriptions. This would provide companies a quick and accurate way to measure customer satisfaction without needing manual inspection and analysis. We used the LSTM (Long Short Term Memory), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit) designs, three widely used recurrent neural network (RNN) architectures, to do this. Tasks requiring sequence modeling, such as natural language processing, are ideally suited for these systems. The "Amazon Fine Food Reviews" dataset that we acquired from Kaggle was first preprocessed. In order to do this, the dataset needed to be cleaned up by having the comments' special characters and punctuation removed. To create a fair and understandable dataset, we additionally chose a subset of the data depending on the duration of the reviews. In addition, we used word clouds for exploratory data analysis to understand the distribution of the most common terms. Next, we utilized the preprocessed dataset to train our LSTM, RNN, and GRU models. Based on the input text descriptions, these models were trained to predict the total review score. In order to reduce the loss value and increase accuracy, the model parameters were optimized throughout the training phase. We evaluated the effectiveness of our models utilizing the testing group. The findings demonstrated that our model produced effectively a loss value of 0.2 for the testing group. This suggests that our algorithm can pretty accurately and effectively estimate the overall review score based on the text descriptions.This approach may be applied practically to automate the generation of an overall evaluation score in the food sector. The model may provide an impartial evaluation of customer happiness by examining the text descriptions that consumers have submitted. Based on user input, it can assist companies in monitoring and enhancing the quality of their goods or services. Overall, our study shows that LSTM, RNN, and GRU models are capable of accurately predicting review scores based on text descriptions. The model's accuracy and loss values suggest that it may be useful in automating review analysis and measuring customer happiness.