Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method

Abstract

Shopee is e-commerce widely accessed and used in this era. Many people use Shopee because the products offered are cheaper and more affordable. Despite the fact that Shopee is a well-known e-commerce, it still requires responses and suggestions from the public to maintain or improve the features required. In this study, public sentiment analysis was carried out on Twitter social media related to the Shopee marketplace. This study collected data that contained tweets from predetermined keywords and used Word2Vec and Support Vector Machine classification methods. The use of Word2Vec influenced the level of accuracy so that it increased for each SVM kernel. Meanwhile, the best hyperparameter tuning was found in the polynomial kernel, with an accuracy rate of 93.20%.

Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis

“…By analyzing the performance results through the confusion matrix, researchers gain an in-depth understanding of the accuracy, precision, recall, and F1score, which provides an overall assessment of how effective the model is and also identifies possible areas for improvement. These metrics are calculated using Equations ( 5), ( 6), (7), and (8). The following results from the confusion matrix on the kernel polynomial model achieved 90% accuracy, 88% precision, 96% recall and 92% f1-score for negative sentiment detection.…”

“…Research related to the shopee product review marketplace where the evaluation results get an accuracy rate of 90.03% using naïve Bayes [7]. Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method kernel SVM accuracy rate 93.20% [8].. Penelitian berikutnya melakukan analisis fake review e-commerce, dengan hasil akurasi terbaik pada model SVM [9]. Penelitian selanjutnya B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM dengan hasil model terbaik pada methode SVM [10].…”

“…Precision: Comparison between correctly classified True Positive cases and the total predicted positive cases (7). F1-Score: Balance the average weight value between precision and recall(8).…”