Emotion Classification through Product Consumer Reviews

Abstract

Currently, the world is becoming digitalized. Ecommerce is enamoring strength in this digitalized world through the accessibility of products reachable for customers. The recent work in the field of emotion classification based on consumer feedback reviews is gaining popularity worldwide. However, the research lacks to provide better accuracy in terms of pre-processing and feature extraction. Since, to determine consumer emotions in terms of their choices, feature extraction and pre-processing plays an important role to enhance the accuracy rate to determine their choices for product marketability and to identify consumer behavior in selecting product features resulting in increased product demands. The review of related literature shows that the products accuracy rate can be improved though refining the dataset by executing proper pre-processing techniques like removing null values and meaning less values from the dataset to make it more accurate and examining its feature extraction by applying different algorithms which required a lot of pre-processing of reviewed data to make the features more valuable. This research provides an analysis of cars, hotels and mobile datasets and studies sentiment classification with different machine learning Approaches. First of all, to minimize the noise of the dataset was undergoing with pre-processing steps including punctuation, stop words, null entries and duplication removal. In the next step, extract the feature by two different methods: Count Vectorizer and bag of words. After that bidirectional encoder representation from transformers (BERT) algorithm were applied on three datasets to predict the results. By applying BERT Classifier on cars dataset 98.5% accuracy were founded, by applying same algorithm on hotels dataset 98.3% accuracy were founded, and for Mobile dataset 98.7% accuracy were founded.