Emotions in Text: Dimensional and Categorical Models

Calvo, Rafael A.; Kim, Sunghwan Mac

doi:10.1111/j.1467-8640.2012.00456.x

Cited by 154 publications

(75 citation statements)

References 39 publications

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“…Further, we analyzed timing information and surface features of communication style such as wordiness and usage of emoticons. Correct detection of all expressions of affect, linguistic and discourse-related cues cannot be guaranteed, however, this set of tools and resources has been successfully applied in numerous psychological experiments and extensively evaluated and validated [35], [38], [39], [40], [46], [47], [48], [49] supporting their application for the automatic analysis of text in different domains.…”

Section: Comparisons Between System and Woz Datamentioning

confidence: 99%

Affect and Social Processes in Online Communication--Experiments with an Affective Dialog System

Skowron

Theunis

Rank

et al. 2013

IEEE Trans. Affective Comput.

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Abstract-This paper presents an integrated view on a series of experiments conducted with an affective dialog system, applied as a tool in studies of emotions and social processes in online communication. The different realizations of the system are evaluated in three experimental setups in order to verify effects of affective profiles, as well as of fine-grained communication scenarios on users' expressions of affective states, experienced emotional changes, and interaction patterns. Results demonstrate that the system applied in virtual reality settings matches a Wizard-of-Oz in terms of chatting enjoyment, dialog coherence and realism. Variants of the system's affective profile significantly influence the rating of chatting enjoyment and an emotional connection. Self-reported emotional changes experienced by participants during an interaction with the system are in line with the type of applied profile. Analysis of interaction patterns, i.e., usage of particular dialog act classes, word categories, and textual expressions of affective states for different scenarios, demonstrates that a communication scenario for social sharing of emotions was successfully established. The experimental evidence provides valuable input for applications of affective dialog systems and strengthens them as valid tools for studying affect and social aspects in online communication.Index Terms-Affective dialog system, human-computer interaction, affect sensing and analysis, structuring affective interactions.

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Section: Comparisons Between System and Woz Datamentioning

confidence: 99%

Affect and Social Processes in Online Communication--Experiments with an Affective Dialog System

Skowron

Theunis

Rank

et al. 2013

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

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“…These thesauri can be used to group words, emotion words, for example, according to their valence or activation (i.e. arousal) (Calvo and Kim 2013).…”

Section: Feature Extractionmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

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225

148

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Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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“…LSA, PLSA and NMF dimensionality reduction techniques are employed to evaluate the recognition. Their best result was related to Anger/Disgust and Joy with the accuracy of 77.3 % by using NMF-based categorical classification (CNMF) [33]. Their achieved performance is near to Li et al (2008) with 75.9 on distress.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 94%

“…Calvo and MacKim [33] developed two different computational models based on NLP for emotion recognition. They used four datasets with an emotional thesaurus and a bag of words.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment

Bakhtiyari

Taghavi

Husain

2014

Neural Comput & Applic

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Emotions play an important role in human interactions. They can be integrated into the computer system to make human-computer interaction (HCI) more effective. Affective computing is an innovative computational modeling and detecting user's emotions to optimize system responses in HCI. However, there is a trade-off between recognition accuracy and real-time performance in some of the methods such as processing the facial expressions, human voice and body gestures. Other methods lack efficiency and usability in real-world applications such as natural language processing and electroencephalography signals. To accomplish a reliable, usable and high-performance system, this paper proposes an intelligent hybrid approach to recognize users' emotions by using easily accessible and low computational cost input devices including keyboard, mouse (touch pad: single touch) and touch screen display (single touch). Using the proposed approach, the system is developed and trained in a supervised mode by artificial neural network and support vector machine (SVM) techniques. The result shows an increase in accuracy of 6 % (93.20 %) by SVM in comparison with the currently existing methods. It is a significant contribution to show new directions of future research in emotion recognition, user modeling and emotional intelligence.

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Emotions in Text: Dimensional and Categorical Models

Cited by 154 publications

References 39 publications

Affect and Social Processes in Online Communication--Experiments with an Affective Dialog System

Affect and Social Processes in Online Communication--Experiments with an Affective Dialog System

Natural language processing in mental health applications using non-clinical texts

Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment

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