  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.
