Knowledge discovery from social media using big data-provided sentiment analysis (SoMABiT)
Knowledge discovery from social media using big data-provided sentiment analysis (SoMABiT)
复制标题
DOI：
10.1177/0165551515602846
复制
发表时间：
2015-12-01
影响因子：
2.4
通讯作者：
Fathi, Madjid
中科院分区：
文献类型：
--
作者：
Bohlouli, Mahdi;Dalter, Jens;Fathi, Madjid
在当今竞争激烈的商业世界中，了解客户需求和以市场为导向的生产是行业成功的关键因素。为此，使用高效的分析算法可确保更好地了解客户反馈，并改进下一代产品。因此，社交媒体在日常生活中的使用急剧增加，为市场分析提供了有益的来源。然而，如何将传统的分析算法和方法扩展到这些不同的多结构数据源是一个重大挑战。本文介绍并讨论了SoMABiT作为一个使用大数据技术的社交媒体分析平台的技术和科学重点。情绪分析已被用于从社交媒体中发现知识。与最先进的技术相比，使用MapReduce和开发分布式算法以实现可以扩展任何数据量并提供社交媒体驱动的知识的集成平台是所提出概念的主要新奇。
In today's competitive business world, being aware of customer needs and market-oriented production is a key success factor for industries. To this aim, the use of efficient analytical algorithms ensures better understanding of customer feedback and improves the next generation of products. Accordingly, the dramatic increase in the use of social media in daily life provides beneficial sources for market analytics. Yet how traditional analytic algorithms and methods can be scaled up for such disparate and multistructured data sources is a major challenge. This paper presents and discusses the technological and scientific focus of SoMABiT as a social media analysis platform using big data technology. Sentiment analysis has been employed in order to discover knowledge from social media. The use of MapReduce and the development of a distributed algorithm towards an integrated platform that can scale for any data volume and provide social media-driven knowledge is the main novelty of the proposed concept in comparison to the state-of-the-art technologies.