Analyzing Customer Sentiment Towards Marketplace Reviews Using Classification Algorithms
DOI:
https://doi.org/10.57152/ijatis.v2i1.1774Keywords:
Classification, Customer, Lazada, Sentiment Analysis, ShopeeAbstract
Numerous online marketplaces like Shopee and Lazada have been developed in Indonesia due to the rapid growth of e-commerce. The Shopee and Lazada apps link buyers and sellers in transactions to purchase and sell products and services. About 100 million users have downloaded both applications as of this writing. Since releasing these programs, the community has voiced various thoughts and complaints. Based on this, user sentiment regarding the Shopee and Lazada applications on the Google Play Store is determined using sentiment analysis using the K-Nearest Neighbor (KNN), Nave Bayes, and Support Vector Machine (SVM) algorithms. Data selection, pre-processing, transformation, data mining, and assessment are the five stages of the Knowledge Discovery in Databases (KDD) approach. For each E-commerce application, 2000 reviews were used as the data. With an accuracy of 85.71% for Gaussian-NB modeling for the Lazada dataset and an accuracy of 85.67% for Bernoulli-NB modeling for the Shopee dataset, the Naive Bayes algorithm has the highest accuracy in experiments on each dataset.
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