Text-based Emotion Aware Recommender

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.Comment: 13 pages, 8 tables, International Conference on Natural Language Computing and AI (NLCAI2020) July25-26, London, United Kingdo

The grass is greener on the other side: understanding the effects of green spaces on Twitter user sentiments

Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research

Methods for Affective Content Analysis and Recognition in Film

The research presented in this thesis resulted from the growing attention on the effects of emotion on users, raising questions about their potential application to computational systems. This research investigates the best methods for determining affective scoring for video content, specifically films. This resulted in the affective video system (AVS) framework, AVS dataset and AVS systems being developed, leading to several contributions to knowledge about the best affective methods and systems. This work presents the necessary theory to understand the subject area. It builds as the thesis matures, laying a pathway in the form of a methodology framework for viewing affective problems and systems, moving into a subsequent study reviewing the well-recognised affective methods such as the International Affective Picture System (IAPS) and how its well-defined processes and procedures could be adapted for a more modern approach using video content. The research then studies the most critical perceivable features from video clips for users, which were analysed using the repertory grid approach. This led to the above contributions being combined to create the AVS system and database, which is a unique database comprising the affective scores for various film clips. This research concluded with the presentation of the best regression methods resulting from this research and its datasets and a summary of this performance, and discussions of the results in terms of other research in this area

Mining Affective Context in Short Films for Emotion-Aware Recommendation

Proceedings of the 26th ACM Conference on Hypertext and Social Media, Middle East Technical University Northern Cyprus Campus, Cyprus, 1-4 September 2015Emotion is fundamental to human experience and impactsour daily activities and decision-making processes where,e.g., the affective state of a user influences whether or notshe decides to consume a recommended item movie, book,product or service. However, information retrieval and recommendationtasks have largely ignored emotion as a sourceof user context, in part because emotion is difficult to measureand easy to misunderstand. In this paper we explore therole of emotions in short films and propose an approach thatautomatically extracts affective context from user commentsassociated to short films available in YouTube, as an alternativeto explicit human annotations. We go beyond the traditionalpolarity detection (i.e., positive/negative), and extractfor each film four opposing pairs of primary emotions:joysadness, angerfear, trustdisgust, and anticipationsurprise. Finally, in our empirical evaluation, we show howthe affective context extracted automatically can be leveragedfor emotion-aware film recommendation.Science Foundation Irelan

Multidimensional opinion mining from social data

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This thesis 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 textual, visual, and audio. Mining people’s social opinions from social sources, such as social media platforms and newswires commenting sections, is a valuable business asset that can be utilised in many ways and in multiple domains, such as Politics, Finance, and Government. The main objective of this research is to investigate how a multidimensional approach to Social Opinion Mining affects fine-grained opinion search and summarisation at an aspect-based level and whether such a multidimensional approach outperforms single dimension approaches in the context of an extrinsic human evaluation conducted in a real-world context: the Malta Government Budget, where five social opinion dimensions are taken into consideration, namely subjectivity, sentiment polarity, emotion, irony, and sarcasm. This human evaluation determines whether the multidimensional opinion summarisation results provide added-value to potential end-users, such as policy-makers and decision-takers, thereby providing a nuanced voice to the general public on their social opinions on topics of a national importance. Results obtained indicate that a more fine-grained aspect-based opinion summary based on the combined dimensions of subjectivity, sentiment polarity, emotion, and sarcasm or irony is more informative and more useful than one based on sentiment polarity only. This research contributes towards the advancement of intelligent search and information retrieval from social data and impacts entities utilising Social Opinion Mining results towards effective policy formulation, policy-making, decision-making, and decision-taking at a strategic level