Discovery of knowledge of associative relations using opinion mining based on a health platform

With the development of ubiquitous computing, people easily access and share a variety of information through searches. Based on the social streams collected through the news, Twitter, web, internet communities, SNS (social network services), and internet boards, accurately searching for information in accordance with the user preferences is necessary. The volume of accumulated social streams rises rapidly with time, and the quality of the referred contents tends to be lowered by information noise, despite their frequencies. Therefore, this study focuses on the discovery of knowledge of associative relations using opinion mining on issues related to health. The proposed method mines rules and discovers knowledge through association analysis and opinion mining of social streams. Correspondingly, unstructured data for three major chronic diseases, namely, high blood pressure, diabetes, and hyperlipidemia, are collected with the use of a crawler. The extracted corpus is used to create transactions, and the association rules of the health corpus are mined. Sets of words with associations are organized to support the decision-making for the choice of words for use in a search engine. The mined association rules of the health corpus are based on relations of words, and meaningful relations are discovered based on opinion mining, i.e., based on a method of analyzing positive or negative aspects of formulated expressions in documents. To achieve this, vocabularies of a sentiment dictionary are used to calculate a frequency-based polarity value and a term frequency–inverse rule frequency (TF–IRF) weight. With the calculated polarity value and TF–IRF weight, the degree of opinion for specific words is drawn from association rules. In this manner, it is possible to express a positive or negative relation between words in a visual manner. Accordingly, the use of association rules and opinion degrees allows the generation of an opinion tree. This helps the conduct of an efficient information search for matters related to health and the formulation of opinion relations from an opinion knowledge tree so as to support decision-making. For performance evaluation, predictions were made in regard to the proposed method, and opinions of test sentences were evaluated. As a result, the precision and recall were excellent. By applying the opinion mining–based knowledge to matters relevant to health, it is possible to reach an accurate decision. In addition, with an inference engine, it is possible to provide a customized UI/UX in an ambient context and thus create added value in health services.