IMPLEMENTATION OF NATURAL LANGUAGE PROCESSING (NLP) IN CONSUMER SENTIMENT ANALYSIS OF PRODUCT COMMENTS ON THE MARKETPLACE

Nadya Alinda Rahmi; Rahmatia  Wulan Dari

doi:10.52436/1.jutif.2024.5.3.1666

Nadya Alinda Rahmi Information System Department, Computer Science Faculty, Putra Indonesia University YPTK Padang, Indonesia
Rahmatia Wulan Dari Information System Department, Computer Science Faculty, Putra Indonesia University YPTK Padang, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.1666

Keywords: consumer sentiment analysis, marketplace, natural language processing (NLP), product comment

Abstract

Market product reviews are invaluable information if processed carefully. The process of analyzing product reviews is more than just considering star ratings; Comprehensive examination of the overall content of review comments is essential to extracting the nuances of meaning conveyed by the reviewer. The problem currently occurring in analyzing reviews of product purchases in the marketplace is the large number of abbreviations and non-standard language used by commenters, making it difficult for the system to understand. Therefore, a Natural Language Processing (NLP) approach is needed to improve the language in the content of review comments so as to achieve maximum performance in sentiment analysis. This research utilizes the KNN and TF-IDF algorithms, coupled with NLP techniques, to categorize Muslim fashion product reviews into two different groups that is positive and negative. The NLP-enhanced classification achieved 76.92% accuracy, 80.00% precision, and 74.07% recall, surpassing the results obtained without NLP, which had 69.23% accuracy, 80.00% precision, and 64.52 recall. %. Frequently appearing words in reviews serve as a description of collective buyer sentiment regarding the product. Positive reviews indicate customer satisfaction with the quality, speed of delivery, and price of the goods, while negative reviews indicate dissatisfaction with factors such as color differences and differences in the number of items received.

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