# Over a Decade of Social Opinion Mining: A Systematic Review

arXiv: https://arxiv.org/abs/2012.03091 · HTML: https://mlsanity.com/paper/2012.03091/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis](https://mlsanity.com/paper/2505.11401/) (2025)
- [BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis](https://mlsanity.com/paper/2311.07296/) (2023)
- [EvoTaxo: Building and Evolving Taxonomy from Social Media Streams](https://mlsanity.com/paper/2603.19711/) (2026)
- [Latent Aspect Detection from Online Unsolicited Customer Reviews](https://mlsanity.com/paper/2204.06964/) (2022)
- [Recognition of Mental Adjectives in An Efficient and Automatic Style](https://mlsanity.com/paper/2307.11767/) (2023)
- [Sentiment Analysis in Digital Spaces: An Overview of Reviews](https://mlsanity.com/paper/2310.19687/) (2023)

## Abstract (arXiv)

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.