Abstract
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.
Highlights
Deep Learning outperforms machine learning approaches to text classification, such as sentiment analysis [1]
Recurrent Neural Networks is a method of deep neural networks applied for text classification that can connect previous information to the current activity [2]
This study focuses on aspect-based sentiment analysis for users' satisfaction of the Netflix application in Indonesia with the Long short-term memory (LSTM) model
Summary
Deep Learning outperforms machine learning approaches to text classification, such as sentiment analysis [1]. RNN has a problem in the learning process, namely vanishing gradient, which makes the learning process difficult [3]. Long short-term memory (LSTM) is one of the RNN architectures that can overcome the vanishing gradient problem and has good performance in text classification because it can represent sentences sequentially [4]. Companies use sentiment Analysis (SA) to understand customer opinions on a product [6]. The customer opinion can be used to make competitor analysis which is essential in the design of a product to be accepted by the public [7]. Sentiment analysis is applied to identify positive, negative and neutral sentiments related to comments, reviews, tweets, feedback and even harsh words according to the sentiments conveyed by users or customers [8]
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