Analyzing Big Data Using Recent Machine Learning Techniques to Assist Consumers in Online Purchase Decision

Abstract

Sentiments can be expressed in a variety of ways like angry, happy, sad, surprised, etc. Recent machine learning (ML) algorithms classify sentiments and assist customers in their purchase decision. Many organizations are predicting the possible correlation between growth of the business and customer satisfaction. On different social media platforms, customers give ‘ratings' to a specific product or service. ML helps in knowing the reasons and assists the businesses to improvise in the weaker sections. Natural language processing integrates data and applies “tokenization” to extract the tokens (words) from the datasets (feedbacks). A set of positive, negative, and neutral words and sentences can be compared to find the relevance. Naïve Bayes classifier, KNN classifier, etc. help knowing the trend and processes the large volume of data in minimal time. This approach helps increasing the predictive power of the model and tests remaining data. Bayesian factor robustness helps analyzing different attribute specifications from the large volume.