Abstract
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.
Highlights
Social media is increasing in popularity and in its importance
Kaplan and Haenlein define social media as ‘‘a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, which allows the creation and exchange of user generated content’’ (Kaplan and Haenlein 2010). This definition fully reflects that social media platforms are essential for online users to submit their views and read the ones posted by other people about various aspects and/or entities, such as opinions about a political party they are supporting in an upcoming election, recommendations of products to buy, restaurants to eat in and holiday destinations to visit
Our Social Opinion Mining (SOM) research focuses on microposts—i.e. information published on the Web that is small in size and requires minimal effort to publish (Cano et al 2016)—that are expressed by individuals on a microblogging service, such as Sina Weibo5 or Twitter and/ or a social network service that has its own microblogging feature, such as Facebook6 and LinkedIn
Summary
Social media is increasing in popularity and in its importance. This is principally due to the large number of people who make use of different social media platforms for various types of social interaction. Kaplan and Haenlein define social media as ‘‘a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, which allows the creation and exchange of user generated content’’ (Kaplan and Haenlein 2010) This definition fully reflects that social media platforms are essential for online users to submit their views and read the ones posted by other people about various aspects and/or entities, such as opinions about a political party they are supporting in an upcoming election, recommendations of products to buy, restaurants to eat in and holiday destinations to visit. Information fusion is the field tasked with researching about efficient methods for automatically or semi-automatically transforming information from different sources into a single coherent representation, which can be used to guide the fusion process This is important due to the diversity in data in terms of content, format and volume (Balazs and Velasquez 2016). Our SOM research focuses on microposts—i.e. information published on the Web that is small in size and requires minimal effort to publish (Cano et al 2016)—that are expressed by individuals on a microblogging service, such as Sina Weibo or Twitter and/ or a social network service that has its own microblogging feature, such as Facebook and LinkedIn
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