Abstract
Nowadays, social media platforms have become a mirror that imitates opinions and feelings about any specific product or event. These product reviews are capable of enhancing communication among entrepreneurs and their customers. These reviews need to be extracted and analyzed to predict the sentiment polarity, i.e., whether the review is positive or negative. This paper aims to predict the human sentiments expressed for beauty product reviews extracted from Amazon and improve the classification accuracy. The three phases instigated in our work are data pre-processing, feature extraction using the Bag-of-Words (BoW) method, and sentiment classification using Machine Learning (ML) techniques. A Global Optimization-based Neural Network (GONN) is proposed for the sentimental classification. Then an empirical study is conducted to analyze the performance of the proposed GONN and compare it with the other machine learning algorithms, such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). We dig further to cross-validate these techniques by ten folds to evaluate the most accurate classifier. These models have also been investigated on the Precision-Recall (PR) curve to assess and test the best technique. Experimental results demonstrate that the proposed method is the most appropriate method to predict the classification accuracy for our defined dataset. Specifically, we exhibit that our work is adept at training the textual sentiment classifiers better, thereby enhancing the accuracy of sentiment prediction.
Highlights
Sentiment Analysis (SA) is a systematic study of the collection and classification of product reviews on various e-commerce platforms [1]
The experimented data has been collected from Amazon for beauty product reviews posted by reviewers
Table 2. shows an overview of the product reviews after assessing positive and negative labels based on ratings
Summary
Sentiment Analysis (SA) is a systematic study of the collection and classification of product reviews on various e-commerce platforms [1]. As the online business has become more popular these days, both sellers and customers are interested in asking and providing feedback on e-commerce platforms simultaneously. These opinions and reviews are a kind of verbal communication that includes personalized suggestions and product ratings. These reviews are a guiding tool for companies to improve their product quality and services. They are very beneficial for consumers to help in making decisions regarding the specific product [2]. Various social media and e-commerce platforms provide reviews and ratings of different types and brands of cosmetics products to consumers
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