A Hybrid Approach for Emotion Detection in Support of Affective Interaction

Gievska, Sonja; Koroveshovski, Kiril; Chavdarova, Tatjana

doi:10.1109/icdmw.2014.130

Cited by 22 publications

(4 citation statements)

References 27 publications

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“…The total number of tweets of social media user's hash-marking emotions that specifically reference emotions inside the tweet itself is small (Mohammad, 2012). In a research conducted on the dataset of ISEAR (International Survey on Emotion Antecedents and Reactions), the emotions extracted using Lexicons yielded more reliable results than the emotions extracted based on machine learning techniques (Gievska et al, 2014). The ISEAR dataset contains a large number of personal reports on circumstances linked to seven emotions, that is, joy, fear, anger, sadness, disgust, shame, and guilt, solicited from more than 3000 students from all over the world.…”

Section: Emotion Codingmentioning

confidence: 99%

Emotions During the COVID-19 Crisis: A Health Versus Economy Analysis of Public Responses

Vemprala

Bhatt

Valecha

et al. 2021

American Behavioral Scientist

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People all over the world were under severe stress and were concerned about their health after a devastating pandemic struck the world in the form of a novel coronavirus disease (COVID-19) in late December 2019. Many nations imposed strict lockdowns and quarantines, causing citizens to maintain social isolation, throwing many companies to a halt. Thousands of people took to Twitter during these challenging circumstances to express their feelings about being caught in the middle of a storm. Twitter witnessed an outpouring of emotions ranging from fear, anger, and sadness associated with the spread of a novel virus that has no known cure, to voices of support and trust for nations’ official response to the pandemic. In studying the emotional response (anger, fear, and sadness) on Twitter about the COVID-19 crisis, we thus see a tale of two crises unfold—choosing health or economy. We capture collective emotions on social media and investigate the patterns and impact of these negative emotions during various stages of the disease outbreak. It also provides crucial insights to health officials and government agencies on communicating crisis information to the public via social media.

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Section: Emotion Codingmentioning

confidence: 99%

Emotions During the COVID-19 Crisis: A Health Versus Economy Analysis of Public Responses

Vemprala

Bhatt

Valecha

et al. 2021

American Behavioral Scientist

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“…Another study [15] compared the performance of different classifiers (Bayesian, random forest, logistic regression, and support vector machine (SVM)) while classifying OSN texts according to Plutchik's wheel of emotions [20]. In addition to pure machine learning approaches, [21] compared a lexicon-based approach (NRC lexicon) to three machine learning algorithms (SVM, Naive Bayes and Decision Tree). The dataset used for the experimental phase was ISEAR 1 (International Survey on Emotion Antecedents and Reactions) which contains a large number of personal reports of people who were asked to write a short account on an event in which they experienced joy, fear, anger, sadness, disgust, shame, or guilt.…”

Section: Related Workmentioning

confidence: 99%

Identifying Emotions in Social Media: Comparison of Word-Emotion Lexicons

Kušen

Cascavilla

Figl

et al. 2017

2017 5th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our survey.

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“…Some researchers opted for a hybrid approach, making use of both supervised and unsupervised methods to achieve a higher accuracy. This is the case of Gievska et al (2014), who designed an emotion detection approach to deal with the ISEAR dataset. 2 This study considered seven emotions derived from Ekman's six emotional categories (Ekman 1994): anger, fear, sadness, disgust, joy, surprise, and an additional neutral category in order to reduce the effect of misclassified data.…”

Section: Related Workmentioning

confidence: 99%

AriEmozione 2.0: Identifying Emotions in Opera Verses and Arias

Zhang¹,

Fernicola²,

Garcea³

et al. 2022

ijcol

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We present the task of identifying the emotions conveyed by the lyrics of Italian opera arias. We shape the task as a multi-class supervised problem, considering the six emotions from Parrot's tree: love, joy, admiration, anger, sadness, and fear. We manually annotated an opera corpus with 2.5k instances at the verse level and experimented with different classification models and representations to identify the expressed emotions. Our best-performing models consider character 3-gram representations and reach relatively low levels of macro-averaged F 1 . Such performance reflects the difficulty of the task at hand, partially caused by the size and nature of the corpus: relatively short verses written in 18th-century Italian. Building on what we learned from the verse-level setting, we adopt a higher granularity and increase the size of the corpus. First, we switch from verses to arias in order to have longer and more expressive texts. Second, we construct a new corpus with 40k arias (∼ 90k verses). This new dataset contains silver data, annotated by self-learning on the basis of an ensemble of binary classifiers.We then experiment with more sophisticated representations, by learning an embedding space and using it to train new models for the identification of emotions at the aria level, obtaining a significant performance boost.

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A Hybrid Approach for Emotion Detection in Support of Affective Interaction

Cited by 22 publications

References 27 publications

Emotions During the COVID-19 Crisis: A Health Versus Economy Analysis of Public Responses

Emotions During the COVID-19 Crisis: A Health Versus Economy Analysis of Public Responses

Identifying Emotions in Social Media: Comparison of Word-Emotion Lexicons

AriEmozione 2.0: Identifying Emotions in Opera Verses and Arias

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