Sentiment Analysis of Different E-commerce platform reviews using Machine Learning Algorithm
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I.O.E. Pulchowk Campus
Abstract
E-commerce provides different products/services to the customer where customers can
easily get their desired products anywhere they want. While buying online, they rely on the
product reviews made by other users which gives much more emphasis on the product
review as it is required for the selection of a product. For the analysis of such reviews,
sentiment analysis is done. Since the data are in huge numbers, machine learning
algorithms are used for the fast and effective calculation and analysis of these product
reviews. These reviews can be done quickly using machine learning as the model is created
where thousands of reviews are made. For the better accuracy of the model, the datasets are
subjected to different pre-processing techniques. Then, both supervised and unsupervised
learning methods as well as the deep learning method to classify the sentiments of the
dataset in positive or negative class. For the validation of our model, secondary dataset
were obtained from the ecommerce platforms like Daraz. For supervised learning models,
we have used Naive Bayes and SVM classifiers. From lexicon based analysis, VADER
classifier is used which exhibits 68% accuracy when validating with the secondary data.
Also supervised algorithms like SVM classifiers exhibit 71% accuracy whereas
Naive-Bayes classifiers exhibit 68% accuracy for the data gathered. But the highest
accuracy was obtained from deep learning models which exhibit 75% accuracy.
Description
E-commerce provides different products/services to the customer where customers can
easily get their desired products anywhere they want. While buying online, they rely on the
product reviews made by other users which gives much more emphasis on the product
review as it is required for the selection of a product.