Analysis of Public Perception on Organic Coffee through Text Mining Approach using Naïve Bayes Classifier

I. Nuritha, A. A. Arifiyanti, Vandha Widartha
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引用次数: 4

Abstract

Productivity of organic coffee plants in Indonesia is still lower if compared by productivity of coffee which use ordinary cultivation. One of the problems, which is faced by farmers to develop organic coffee is no certainty market. This could be because not all people of Indonesia are able to buy organic coffee products which quite expensive. Based on these, it is necessary to analyze public perception sentiment of organic coffee products, to identify potential and opportunities the development of organic coffee farming in Indonesia. This research uses a text mining approach to classify the public perception sentiment on organic coffee products based on tweet which posted in social media, i.e., twitter. Sentiment classification is performed by Naïve Bayes Classifier algorithm. The most of sentiment value formed in this research is positive sentiment. These results show that the public perception on organic coffee is in positive manner. So that the prospect of organic coffee plants development in Indonesia and the market opportunity of organic coffee products are predicted to rise as well.
使用Naïve贝叶斯分类器的文本挖掘方法分析公众对有机咖啡的认知
如果与普通种植的咖啡相比,印尼有机咖啡的产量仍然较低。农民发展有机咖啡面临的问题之一是市场不确定。这可能是因为不是所有的印尼人都能买到非常昂贵的有机咖啡产品。在此基础上,有必要分析公众对有机咖啡产品的认知情绪,以识别印尼有机咖啡种植发展的潜力和机遇。本研究采用文本挖掘的方法,对公众对有机咖啡产品的感知情绪进行分类,该分类基于社交媒体(即twitter)上发布的tweet。情感分类采用Naïve贝叶斯分类器算法。本研究所形成的情感价值以积极情绪为主。这些结果表明,公众对有机咖啡的认知是积极的。因此,有机咖啡在印尼的发展前景和有机咖啡产品的市场机会也将增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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