Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach

Mukherjee, Rajdeep; Naik, Ajit Kumar; Poddar, Sriyash; Dasgupta, Soham; Ganguly, Niloy

doi:10.1145/3404835.3463080

Cited by 12 publications

(10 citation statements)

References 34 publications

(36 reference statements)

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“…They do so using a pretrained transformer-based model (namely RoBERTa, Liu et al 2019), finetuned with earth movers distance (Rubner, Tomasi, and Guibas 2000) as a loss function to perform classification. Related approaches learn multiple emotion models at once, showing that a multi-task learning of discrete categories and VAD scores can benefit both subtasks (Akhtar et al 2019;Mukherjee et al 2021). Particularly interesting for our work is the study by Buechel, Modersohn, and Hahn (2021).…”

Section: Joymentioning

confidence: 98%

Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

Troiano¹,

Oberländer²,

Klinger³

2022

Preprint

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The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An important observation for natural language processing is that emotions can be communicated implicitly by referring to events alone, appealing to an empathetic, intersubjective understanding of events, even without explicitly mentioning an emotion name. In psychology, the class of emotion theories known as appraisal theories aims at explaining the link between events and emotions. Appraisals can be formalized as variables that measure a cognitive evaluation by people living through an event that they consider relevant. They include the assessment if an event is novel, if the person considers themselves to be responsible, if it is in line with the own goals, and many others. Such appraisals explain which emotions are developed based on an event, e.g., that a novel situation can induce surprise or one with uncertain consequences could evoke fear. We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories. To achieve that, we compile a corpus by asking people to textually describe events that triggered particular emotions and to disclose their appraisals. Then, we ask readers to reconstruct emotions and appraisals from the text. This setup allows us to measure if emotions and appraisals can be recovered purely from text and provides a human baseline to judge model's performance measures. Our comparison of text classification methods to human annotators shows that both can reliably detect emotions and appraisals with similar performance. We further show that appraisal concepts improve the categorization of emotions in text.

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Section: Joymentioning

confidence: 98%

Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

Troiano¹,

Oberländer²,

Klinger³

2022

Preprint

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“…Sentiment scores [24], topic information [7], [38] and speaker-utterance relations [30] are also leveraged to enhance model performance. As an effective dimensional emotion representation model [39], VAD information is also incorporated to facilitate emotion recognition in multiple modalities, such as text [40], [41], [42] and acoustics [43], [44], which considerably boosts the model performance.…”

Section: Emotion Recognition In Conversationsmentioning

confidence: 99%

“…Multi-task learning was also leveraged to introduce topic information [30], [31], discourse roles [32] and speaker-utterance relations [26] to aid emotion reasoning. [33], [34] incorporated VAD information to introduce fine-grained sentiment supervision. Contrastive learning [12], [35], [36] was also devised to distinguish utterances with similar emotions.…”

Section: Emotion Recognition In Conversationsmentioning

confidence: 99%

Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

Yang

Zhang

Alhuzali

et al. 2023

IEEE Trans. Affective Comput.

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A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information between emotions. ERC is also not equally dependent on all embedded features in the semantic space, which makes the high-dimensional SCL inefficient. To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes. To help modelling the dialogue and enriching the context, we leverage the pre-trained knowledge adapters to infuse linguistic and factual knowledge. Experiments show that our method achieves new state-of-the-art results with 69.81% on IEMOCAP, 65.7% on MELD, and 62.51% on DailyDialog datasets. The analysis also proves that the VAD space is not only suitable for ERC but also interpretable, with VAD prototypes enhancing its performance and stabilising the training of SCCL. In addition, the pre-trained knowledge adapters benefit the performance of the utterance encoder and SCCL. Our code will be available online upon acceptance.

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“…This is understandable since we do not utilize any specialized sentiment modeling technique. In future, we propose to utilize word-level Valence, Arousal, Dominance scores (Mukherjee et al, 2021a) as additional features to better capture the sentiment of the opinion phrase.…”

Section: Robustness Analysismentioning

confidence: 99%

PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

Mukherjee¹,

Nayak²,

Butala³

et al. 2021

Preprint

Self Cite

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Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans. A recent grid tagging approach on the other hand fails to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair. Different from these, we present a tagging-free solution for the task, while addressing the limitations of the existing works. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step thereby making our solution end-to-end. Interactions between the aspects and opinions are effectively captured by the decoder by considering their entire detected spans while predicting their connecting sentiment. Extensive experiments on several benchmark datasets establish the better efficacy of our proposed approach, especially in recall, and in predicting multiple and aspect/opinion-overlapped triplets from the same review sentence. We report our results both with and without BERT and also demonstrate the utility of domainspecific BERT post-training for the task.

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Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach

Cited by 12 publications

References 34 publications

Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

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