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.

Background: In the EU funded MARIO project, specific technological tools are adopted for the people living with dementia (PLWD). In the final stage of the project, a validation of the MARIO companion robot was performed from August to October 2017. Objective: The aims of the present study are: 1) to illustrate the key results and evidence obtained in the final evaluation phase of the project across the three different pilot sites; 2) to assess the engagement dimensions of the PLWD who interacted with the MARIO robot; and 3) to assess the acceptability and efficacy of the MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience, quality of life in PLWD, and burden level of the caregivers. Methods: 38 people (M = 14; F = 24) with Alzheimer’s disease were screened for eligibility and all were included. The following tests were administered Pre and Post interactions with MARIO: Observational Measurement of Engagement (OME), Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Neuropsychiatric Inventory (NPI), Cornell Scale for Depression in Dementia (CSDD), Multidimensional Scale of Perceived Social Support (MSPSS), 14-item Resilience Scale (RS-14), Quality of Life in Alzheimer’s Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out. Results: In Post-MARIO interactions, significant improvements were observed in RS-14 ( p = 0.020).Considering the age of the people, PLWD with 68–76 years perceived that they had major social support (MSPSS Total: p = 0.016) and friends to support them (MSPSS Fri: p = 0.014). Indeed, the younger people (55–67 years) were less depressed (CSDD: p = 0.033), and more resilient (RS-14: p = 0.003). The people aged 77–85 years perceived they had major family support (MSPSS Fam: p = 0.018). The participants were gender and education matched without any statistically significant difference. Conclusion: MARIO may be a useful tool in mitigating depression and loneliness, while enhancing social connectedness, resilience, and overall quality of life for people with dementia.

In this paper, we present a sentiment-annotated Twitter gold standard for the Brexit referendum. The data set consists of 2,000 Twitter messages ("tweets") annotated with information about the sentiment expressed, the strength of the sentiment, and context dependence. This is a valuable resource for social media-based opinion mining in the context of political events.

The use of social media amongst children, adolescents and families is nowadays a common practise in our everyday lives. Social networking sites allow social interaction between people through various channels, such as Twitter, Facebook, YouTube and blogs. Even if this interaction is generally healthy, these sites bring several risks, such as cyberbullying, depression and exposure of inappropriate content. In this paper we tackle the problem of cyberbullying via a novel approach that analyses online posts in trending world events. These generally cause a lot of interest and controversy among online Web users. Twitter is the social network of choice, where a large dataset of tweets is collected. The two current world events selected are the Ebola virus outbreak in Africa and the shooting of Michael Brown in Ferguson, Missouri. Collected tweets are carefully analysed to identify the most popular hashtags and named entities used within cyberbullying tweets. This analysis provides a basis towards several useful applications, such as a cyberbullying online post detector for certain current trending world events. This will help reduce the number of cyberbullying cases in social networking sites. Results obtained from this evaluation can be applied to other cyberbullying scenarios.