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Using Machine Learning to Predict the Sentiment of Online Reviews: A New Framework for Comparative Analysis by Gregorius Satia Budhi; Raymond Chiong; Ilung Pranata; Zhongyi Hu is a Engineering article available to read on EtoBox.

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Online reviews are becoming increasingly important for decision-making. Consumers often refer to online reviews for opinions before making a purchase. Marketers also acknowledge the importance of online reviews and use them to improve product success. However, the massive amount of online review data, as well as its unstructured nature, is a challenge for anyone wanting to derive a conclusion quickly. In this paper, we propose a novel framework for gauging the ratings of online reviews using machine learning techniques. This framework uses a combination of text pre-processing and feature extraction methods. Here, we investigate four different aspects of the new framework. First, we assess the performance of single and ensemble classifiers in predicting sentiment-positive or negative-initially on a specific dataset (Yelp), but subsequently also on two other datasets (Amazon's product reviews and a movie review dataset). Second, using the best identified classifiers, we improve the accuracy with which neutral polarity can be predicted, an ability largely overlooked in the literature. Third, we further improve the performance of these classifiers by testing different pre-processing an

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Author

Gregorius Satia Budhi; Raymond Chiong; Ilung Pranata; Zhongyi Hu

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Engineering (Physical Sciences)