CYBEREMOTIONS – Collective Emotions in Cyberspace

Ahn, Jeong Joon; Borowiec, Anna; Buckley, Kevan; Chmiel, Anna; Czaplicka, Agnieszka; Dąbrowski, Grzegorz; Garas, Antonios; García, David; Gobron, Stéphane; Hillmann, Robert; Hołyst, Janusz A.; Kappas, Arvid; Küster, Dennis; Mitrović, Marija; Paltoglou, George; Pirker, Hannes; Rank, Stefan; Schweitzer, Frank; Sienkiewicz, Julian; Skowron, Marcin; Sobkowicz, Paweł; Thalmann, Daniel; Thelwall, Mike; Theunis, Mathias; Trier, Matthias; Tsankova, Elena; Weroński, Paweł

doi:10.1016/j.procs.2011.09.076

Cited by 4 publications

(3 citation statements)

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“…Applications in businesses include (i) computation of customer satisfaction to get an idea of how happy customers are with a company's products from the ratio of positive to negative tweets about them; (ii) identification of critics and promoters: it can be used by customer support for spotting dissatisfaction or problems with products. Several research groups around the world are currently focusing on understanding the dynamics of sentiment in electronic communities through sentiment analysis and CYBEREMO-TION [27] is one such project funded by the 7th framework initiative of the European Union with participation of nine institutes in Europe. The primary goal of this project is to understand the role of collective emotions in electronic communities.…”

Section: Related Workmentioning

confidence: 99%

Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

Rajput

Haider

Ghani

2016

Applied Computational Intelligence and Soft Computing

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The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students’ feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregated Likert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and other frequency-based filters.

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Section: Related Workmentioning

confidence: 99%

Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

Rajput

Haider

Ghani

2016

Applied Computational Intelligence and Soft Computing

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“…It is relatively simple to design a model that, based on ad hoc assumptions, reproduces any observed macroscopic behaviour. As part of an interdisciplinary collaboration to understand the dynamics of collective emotions in online communities [46], the Cyberemotions modelling framework [11] was designed to provide generative mechanisms of online collective emotions, explicitly avoiding the pitfall of using ad hoc assumptions and implausible dynamics. This framework allows the creation of models of user emotions under different kinds of online interaction, linking collective behaviour with individual dynamics in the presence of online interaction mechanisms.…”

Section: Introductionmentioning

confidence: 99%

The dynamics of emotions in online interaction

et al. 2016

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We study the changes in emotional states induced by reading and participating in online discussions, empirically testing a computational model of online emotional interaction. Using principles of dynamical systems, we quantify changes in valence and arousal through subjective reports, as recorded in three independent studies including 207 participants (110 female). In the context of online discussions, the dynamics of valence and arousal is composed of two forces: an internal relaxation towards baseline values independent of the emotional charge of the discussion and a driving force of emotional states that depends on the content of the discussion. The dynamics of valence show the existence of positive and negative tendencies, while arousal increases when reading emotional content regardless of its polarity. The tendency of participants to take part in the discussion increases with positive arousal. When participating in an online discussion, the content of participants' expression depends on their valence, and their arousal significantly decreases afterwards as a regulation mechanism. We illustrate how these results allow the design of agent-based models to reproduce and analyse emotions in online communities. Our work empirically validates the microdynamics of a model of online collective emotions, bridging online data analysis with research in the laboratory.

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“…As a result, hot events might cause avalanches of negative feelings and attitudes. An examination of top Twitter events revealed that even popular positive events typically result in rises in negative sentiment strength (4). Relevant research, however, shows that both individual and collective emotional states can be controlled.…”

Section: Cyberemotion Regulationmentioning

confidence: 99%

Cyberemotions in the Era of New Media

Zhang¹,

Meng²

2023

Sci Insights

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The introduction of new media has accelerated changes in the generation and delivery of information. Cyberemotions, as a new type of public opinion, can be more contagious and propagate in more diverse ways than traditional public sentiments. The emotions of internet users have a rising influence on the progression of recent significant social events. In order to spark additional discussion on online sentiment modulation and online public opinion tracking, this article presented an overview of cyberemotions definition and key analytical methodologies in existing online sentiment research, as well as a synopsis of major components of cyberemotions.

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CYBEREMOTIONS – Collective Emotions in Cyberspace

Cited by 4 publications

References 0 publications

Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

Lexicon-Based Sentiment Analysis of Teachers’ Evaluation

The dynamics of emotions in online interaction

Cyberemotions in the Era of New Media

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