JOURNAL ARTICLE

Analisis Sentimen Pada Ulasan Produk UNIQLO dengan Algoritma Naive Bayes

Eneng Elsa AmeliaIndra Yustiana

Year: 2024 Journal:   J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol: 8 (1)Pages: 141-141

Abstract

Sentiment analysis is provided by internet users on social media to express personal assessments or opinions. One of the brands that often receives sentiment from users on social media is Uniqlo. Sentiment opinions play a crucial role. In the context of business and information technology, sentiment analysis is often applied to product reviews, customer service, or consumer responses on social media to gather information about how the public perceives a product or brand, which is valuable for both other customers and the store. Currently, the activity of providing product reviews, often referred to as reviews, is gaining attention from many parties and becoming a profession of choice. However, becoming a reviewer requires genuine experience and expertise in the field. This is because reviews, in the form of critiques and suggestions, must be conducted with careful consideration. Those who conduct reviews will adhere to the principles of analysis and facts rather than arbitrary opinions. Reviews, although in the form of concise summaries, can be very useful in various fields, from marketing to the arts. Reviews are a form of evaluation or assessment of a product, service, work of art, book, film, place, or anything else. It involves giving personal opinions or perspectives based on personal experience or knowledge of the subject being reviewed. Reviews can be positive, negative, or neutral depending on the experience, views, or individual perspectives of the reviewer. By using Text Mining classification methods, it is possible to determine whether a sentiment is positive, neutral, or negative. One widely used algorithm in sentiment analysis is the Naïve Bayes classification method.

Keywords:
Naive Bayes classifier Mathematics Computer science Artificial intelligence Support vector machine

Metrics

3
Cited By
4.58
FWCI (Field Weighted Citation Impact)
0
Refs
0.93
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Data Mining and Machine Learning Applications
Physical Sciences →  Computer Science →  Information Systems
Computer Science and Engineering
Physical Sciences →  Computer Science →  Artificial Intelligence
Multimedia Learning Systems
Physical Sciences →  Computer Science →  Information Systems

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