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首页> 外文期刊>International journal of knowledge and systems science >An Association Rule Mining Approach to Discover Demand and Supply Patterns Based on Thai Social Media Data
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An Association Rule Mining Approach to Discover Demand and Supply Patterns Based on Thai Social Media Data

机译：基于泰国社交媒体数据发现需求和供应模式的关联规则挖掘方法

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In the digital age, social media technology has an important role as a communication platform for interpersonal interactions in the online virtual world. In addition, social media has impacted product exchange behavior in both vendors and buyers, with a shift from the traditional sales model to communication between parties via social media. Social media marketing, an online means of buying, selling, and exchanging goods and services, is increasingly popular due to convenience, speed, and greater choices. This trend has grown rapidly and is set to expand, leading to increased interest in research which analyzes and processes social media marketing data to gain a new integrated body of knowledge to better serve online business transactions. This research covers a new field, which may cause research and development limitations requiring background knowledge in several areas, such as digital technology, data analytics, and business analysis. This research aims to develop a framework to search for association rule mining of demand and supply data on social media platforms. Data is collected from Twitter and underwent cleansing and labeling for separating into five groups. Hashtag data from tweets is then extracted and transformed to input attributes of the framework. Next, association rule mining is performed using the Apriori algorithm in order to determine frequent items and extract candidate association rules. The last stage is rule selection, which uses Twitter-specific statistical attributes, that is, number of retweets and likes, to select highly effective association rules. The findings are that it is possible to mine association rules relating to demand and supply on Twitter. Based on an analysis of the association rule results, the content of those rules reflects trending activities and events at different times. Such information can be analyzed in further research to design improvements in social media marketing.
机译：在数字时代，社交媒体技术具有作为在线虚拟世界中的人际关系互动的通信平台的重要作用。此外，社交媒体在供应商和买家的产品交换行为中受到影响，随着传统销售模式转向各方通过社交媒体的沟通。社交媒体营销，购买，销售和交换商品和服务的在线营销，由于方便，速度和更大的选择，越来越受欢迎。这一趋势已经迅速增长，并设定扩展，导致对研究的兴趣增加，这些研究分析和处理社交媒体营销数据，以获得更好地服务于在线业务交易的新综合知识体系。该研究涵盖了一个新的领域，可能导致研究和开发限制需要在几个领域中的背景知识，例如数字技术，数据分析和业务分析。本研究旨在开发一个框架，以搜索社交媒体平台上的需求和供应数据的关联规则挖掘。从Twitter收集数据，并进行清洁和标签，以分为五组。然后提取来自推文的HASHTAG数据并将其转换为框架的输入属性。接下来，使用APRIORI算法执行关联规则挖掘，以便确定频繁的项目并提取候选关联规则。最后阶段是规则选择，它使用特定于特定于Twitter的统计属性，即转发和喜欢的数量，选择高效的关联规则。调查结果是，在推特上挖掘有关需求和供应的关联规则。根据关联规则结果的分析，这些规则的内容反映了不同时间的趋势活动和事件。可以在进一步研究中分析这些信息，以改进社交媒体营销。

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