Sentiment Analysis using Text Mining for Leather Industry Trend

Abstract

In industrial 4.0 era and internet of things, creative industry such as leather industry must always innovate by considering market wants and needs. This article aim to discuss the utilization of sentiment analysis from social media to see see trends in leather that are in the market's interest. We used text mining technique that classify positive, negative, and neutral sentiment about each type of leather, among others: full grain leather, top grain leather, geuine leather, bonded leather, faux/synthetic leather, nubuck leather, suede leather, and patent leather. The result of experiment using RapidMiner showed that suede leather is the most liked with highest positive sentiment percentage value around 67.39%. This sentiment analysis result could be used as recommendation for leather industry to improve the quality of their marketing, because the society opinion was not only about the quality of leather, but also the price and personal taste of leather influence the sentiment result.