Sentiment Analysis Model Using Deep Learning

The customer’s opinion about goods and services is highly valued by both customers and producers. As a result, both industry and academia have invested a lot of time and energy on sentiment analysis. Sentiment analysis offers suggestions for enhancing the quality of the product and aids the consumer in choosing what to buy. What is the use of classifying a statement as positive or negative? Take the Amazon website as an example. On Amazon, customers may submit reviews of products, rating them as either good, terrible, or even neutral. It would be costly and time-consuming to use a human to look through all the comments and compile customer feedback on the item as a whole. Deep learning models are capable of processing enormous amounts of data, drawing conclusions and categorizing comments. The most recent research using deep learning to address issues with sentiment analysis, like sentiment polarity, is reviewed in this paper. A dataset has been subjected to the application of models utilizing Term Frequency-Inverse Document Frequency (TF-IDF), CNN and fission–fusion interactive optimization algorithm. The spider monkey optimization-based deep convolutional neural network is utilized in this study to classify the emotions expressed in the tweets. Using this DL model, businesses like Amazon may improve their products based on user feedback, increasing sales.