Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.

由于人们使用社交媒体跨多种渠道进行各种类型的社交互动，社交媒体的受欢迎程度和重要性正在不断增加。本系统综述重点关注不断发展的社会意见挖掘研究领域，其任务是从多个社交媒体平台上代表的用户生成内容中识别多个意见维度，例如主观性、情感极性、情感、情感、讽刺和反讽。各种媒体格式，如文本、图像、视频和音频。通过社会意见挖掘，可以根据人类表达的不同意见维度来理解自然语言。这有助于人工智能的发展，而人工智能又有助于推进一些现实世界的用例，例如客户服务和决策。对社会意见挖掘研究进行了彻底的系统回顾，该研究总计 485 篇已发表的研究，跨度为 2007 年至 2018 年的 12 年。深入分析的重点是社交媒体平台、技术、社交数据集、语言、情态、衍生的工具和技术等方面。社会意见挖掘可用于许多应用领域，包括产品/服务管理的营销、广告和销售，以及政治、技术、金融、医疗保健、体育和政府等多个领域和行业。 2018 年以后社会舆论挖掘的最新进展也与未来的研究方向一起呈现，旨在在一些现实世界的应用中留下更广泛的学术和社会影响。