Customer Experience Analysis Skincare Products Through Social Media Data Using Topic Modeling and Sentiment Analysis

Abstract

Currently, skin care products (skincare) are popular among the public. Both men and women are interested in buying skin care products. Moreover, there are many brands of skin care products that are divided into several types of facial and body care, such as moisturizers, toners, cleansers, and masks. Therefore, many consumers take the time to find information, for example, in terms of price, quality, and brand for decision-making. A lot of useful information is in the form of Twitter messages known as tweets which are sent from people who use skin care products because Twitter is one of the online social media where users can share their opinions and experiences. However, consumers still have to spend a lot of time searching, reading, and understanding the comprehensive collection of tweets before buying skin care products.The purpose of this study is to analyze customer experience, analyzing automated tweets about skin care products. Tweets about skin care products will be subjected to a topic modeling process to find out what topics are being discussed. In addition, the topics that have been obtained will be subject to sentiment analysis in the form of positive and negative messages for skin care products. Consumers who are app users don’t waste time reading and analyzing large amounts of data manually and they can decide to buy skin care products more easily.The results of this study obtained 14 topics of discussion related to skincare. Meanwhile, the sentiment analysis results of 14 topics resulted in more positive sentiment class tweets overall. It related the category topic that has the number of tweets to the importance of skincare. In addition, categories related to ingredients for skincare products from nature, namely fruits and spices, are the topics that have the second highest number of tweets. The results of the analysis of tweets related to user experience on Twitter, it was found that users prefer skincare products that use ingredients from nature.

Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine

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