Over a Decade of Social Opinion Mining: A Systematic Review

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.

170 pages, 3 figures. This is a preprint of an article published in Artificial Intelligence Review (2021)

References in corpus (6)

• Comparing and Combining Sentiment Analysis Methods
• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
• Multitask Learning for Fine-Grained Twitter Sentiment Analysis
• A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency
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Cited by in corpus (4)

• GraphMFT: A Graph Network based Multimodal Fusion Technique for Emotion Recognition in Conversation
• GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection
• A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends
• Beyond the Lens: Quantifying the Impact of Scientific Documentaries through Amazon Reviews