Annotating affective dimensions in user-generated content

Bruyne, Luna De; Clercq, Orphée De; Hoste, Véronique

doi:10.1007/s10579-020-09524-2

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Transitioning from sentiment to emotion in the context of ABSA leads to ABEA. ABEA shifts the focus from determining sentiment polarity to classifying the emotions related to aspects [9,10]. Table 2 shows the primary datasets of the sentiment analysis and emotion analysis tasks.…”

Section: Aspect-based Sentiment Analysis and Aspect-based Emotion Ana...mentioning

confidence: 99%

“…Emotion analysis classifies emotions into predefined categories such as joy, anger, and sadness, providing a more detailed spectrum [5]. Sentiment analysis and emotion analysis have evolved into Aspect-Based Sentiment Analysis (ABSA) [6][7][8] and Aspect-Based Emotion Analysis (ABEA) [9,10]. ABSA tasks primarily address review data, aiming to identify varying sentiment polarities associated with specific aspects (targets).…”

Section: Introductionmentioning

confidence: 99%

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Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Kang,

Choi,

Kim

2024

Mathematics

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Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations.

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Section: Aspect-based Sentiment Analysis and Aspect-based Emotion Ana...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Kang,

Choi,

Kim

2024

Mathematics

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“…We will discuss this emotion clustering in Section 3.2.6. As for the annotation using the dimensional framework, we decided to only annotate valence and arousal, since various previous studies showed that annotator agreement on dominance was too low to be used for machine learning [51,52].…”

Section: Categorical and Dimensional Emotion Annotationmentioning

confidence: 99%

“…Previous research shows that in the dimensional annotation framework, valence tends to have a higher agreement than arousal [51,52], so we choose valence as a representative of the agreement in dimensional annotations. Also in our experiment, valence annotations are aligned with sentiment polarities, which means we can take valence annotations as sentiment annotations to evaluate the agreement in sentiment polarities.…”

Section: Agreement Studymentioning

confidence: 99%

UniC: a Dataset for Emotion Analysis of Videos with Multimodal and Unimodal Labels

Du,

Labat,

Demeester

et al. 2024

Preprint

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As one of the important features that differentiates humans from machines, emotion is complicated not only in its wide varieties but also in its expression channels, including both verbal and non-verbal language. Different modalities contribute in unique ways to the integrated expression of emotion. However, in most of the existing multimodal datasets, there is only one unified emotion label for the various modalities, ignoring the heterogeneity and complementarity of the different modalities. For instance, the text ''I love the test" may be labelled as love or joy, but if it is expressed with a low dejected tone and a mournful expression, the overall emotion might be more of sadness or even disgust. To bridge this gap, we present in this paper UniC, a novel multimodal emotion dataset featuring both integrated multimodal labels and independent unimodal labels. UniC is comprised of 965 emotion-rich video clips selected from YouTube, annotated in text, audio, (silent) video and multimodal setups with both categorical and dimensional labels. We present the dataset construction steps and an analysis of different modality perspectives based on UniC. It is found that although in most cases the modality of text shares more emotional resemblance with the multimodal setup, other modalities may have different and even opposite emotions which might contribute more to the overall emotion states. This dataset contributes a modality-specific perspective to multimodal emotion analysis, and has the potential to offer more insights for further research in human-machine interaction and emotion modelling for robots.

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EmoTwiCS: a corpus for modelling emotion trajectories in Dutch customer service dialogues on Twitter

Labat,

Demeester,

Hoste

2023

Lang Resources & Evaluation

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Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of online platforms, the automatic detection of emotions forms an important application in monitoring customer satisfaction and preventing negative word-of-mouth. This paper introduces EmoTwiCS, a corpus of 9489 Dutch customer service dialogues on Twitter that are annotated for emotion trajectories. In our business-oriented corpus, we view emotions as dynamic attributes of the customer that can change at each utterance of the conversation. The term ‘emotion trajectory’ refers therefore not only to the fine-grained emotions experienced by customers (annotated with 28 labels and valence-arousal-dominance scores), but also to the event happening prior to the conversation and the responses made by the human operator (both annotated with 8 categories). Inter-annotator agreement (IAA) scores on the resulting dataset are substantial and comparable with related research, underscoring its high quality. Given the interplay between the different layers of annotated information, we perform several in-depth analyses to investigate (i) static emotions in isolated tweets, (ii) dynamic emotions and their shifts in trajectory, and (iii) the role of causes and response strategies in emotion trajectories. We conclude by listing the advantages and limitations of our dataset, after which we give some suggestions on the different types of predictive modelling tasks and open research questions to which EmoTwiCS can be applied. The dataset is made publicly available at https://lt3.ugent.be/resources/emotwics.

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Annotating affective dimensions in user-generated content

Cited by 6 publications

References 24 publications

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

UniC: a Dataset for Emotion Analysis of Videos with Multimodal and Unimodal Labels

EmoTwiCS: a corpus for modelling emotion trajectories in Dutch customer service dialogues on Twitter

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