Analysis of Product Reviews on Machine Learning Vs Deep Learning Performances using Sentiment Analysis

  • U. Preetha, P. Nirupama


At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going  to the shopping  mall.  However,  the  drawback  of  this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform  service  often  provides  the  review  section  to  let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing   models   using   logistic   regression,   naïve   bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed  dataset  and the  non-preprocessed  dataset.  The result is that the bag of words with neural network outperforms other   techniques   on   both   non-preprocess   and   preprocess datasets.

How to Cite
U. Preetha, P. Nirupama. (2023). Analysis of Product Reviews on Machine Learning Vs Deep Learning Performances using Sentiment Analysis. Design Engineering, (1), 277 - 287. Retrieved from