Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model

Nowadays, digital reviews and ratings of E-commerce platforms provide a better way for consumers to buy the products. E-commerce giants like Amazon, Flipkart, etc provide customers with a forum to share their experience and provide potential consumers with true evidence of the product's outcomes. To obtain useful insights from a broad collection of reviews, it is important to separate reviews into positive and negative feelings. In the proposed work, Sentiment Analysis is to be done on the consumer review data and categorize into positive and negative feelings. Naïve Bayes Classification, LSTM and Support Vector Machine (SVM) were employed for the classification of reviews from the various classification models. Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. In this research work, BERT Base Uncased model which is a powerful Deep Learning Model is presented to elucidate the issue of Sentiment Analysis. The BERT model gave an improved performance with good prediction and high accuracy compared to the other methods of Machine Learning in the experimental evaluation.