文章基本信息

标题：Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
本地全文：下载
作者：Gina Khayatun Nufus ; Mustafid Mustafid ; Rahmat Gernowo 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：317
页码：1-6
DOI：10.1051/e3sconf/202131705031
语种：English
出版社：EDP Sciences
摘要：Customer reviews are important information that can be used by providers of goods or services to maintain customer loyalty, understand customer feelings and analyze the business competition. Users use customer feedback on social media or e-commerce as material for consideration before using or buying products. This study aims to conduct a sentiment analysis for user satisfaction of the Video on Demand application in Indonesia with the Long Short-Term Memory (LSTM) model. LSTM, which is a deep learning method that is widely implemented in natural language processing research. Sentiment analysis is applied to find out how customers feel about products on the market. The study results indicate that the LSTM model's implementation for sentiment analysis using two positive and negative labels obtained the value of precision 73.81%, recall 73.81%, f1 score 73.81%. In addition, the accuracy value obtained is 73.90% can be used as a consideration for the company in knowing user sentiment to meet the expectations and desires of customers and keep customers using the service.