Sentiment Analysis of Reviews on Lazada Apps using Naïve Bayes Algorithm

 Zhafran Afif Nurdiyansa (Universitas Amikom Purwokerto, Purwokerto, Indonesia)
 (*)Berlilana Berlilana Mail (Universitas Amikom Purwokerto, Purwokerto, Indonesia)

(*) Corresponding Author

Submitted: December 30, 2023; Published: January 30, 2024

Abstract

Lazada app reviews on the Google Play Store become useful information if processed properly. Existing or new users can analyze app reviews to get information that can be used to evaluate the service. The activity of analyzing app reviews is not enough just to look at the number of stars, it is necessary to look at the entire content of the review comments to be able to know the purpose of the review. A sentiment analysis system is a system used to automatically analyze reviews to obtain information including sentiment information that is part of online reviews. This time the data will be classified using the Naive Bayes method. A total of 1,000 user reviews of the Lazada app were collected to form a dataset. The purpose of this study was to conduct sentiment analysis of Lazada app reviews on Google Play Store using Naive Bayes algorithm. This stage of research involves data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage, there are 6 stages, namely Cleaning, Case Factoring, Word Normalization, Tokenization, Hyphen Removal, and Base Word Formation. The TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighing. The data will be grouped into two categories, namely negative and positive. Next, the data will be evaluated using accuracy parameters. The test results showed an accuracy value of 84%, then for the grouping of negative and positive reviews, it was found that Lazada application reviews tended to be negative.

Keywords


Markeplace; Naïve Bayes; Sentimen Analysis; Pre – Processing; TF-IDF

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