A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories

• URL: http://arxiv.org/abs/2606.29068v1
• Date: Sat, 27 Jun 2026 20:09:05 GMT
• Title: A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories
• Abstract summary: It remains unclear to what extent the latent representations produced by modern text encoders capture well-defined psychological theories of affect.<n>In this work, we investigate the affective capabilities of twelve recently released text encoders by probing their generated embeddings as input features for solving regression and classification tasks.
• Score: 6.880856300506082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text encoders are known for their utility in natural language processing, as they are able to efficiently compress inputs into dense vectors while preserving semantics. These models have been applied to affective computing, in particular to help with solving sentiment analysis and emotion recognition tasks. Nevertheless, it remains unclear to what extent the latent representations produced by modern text encoders capture well-defined psychological theories of affect. In this work, we investigate the affective capabilities of twelve recently released text encoders by probing their generated embeddings as input features for solving regression and classification tasks across three established emotion frameworks, using both word- and sentence-level data. Additionally, we apply a semantic data-leakage prevention technique to improve robustness in word-level evaluations. Our main findings show that the latent manifolds of the latest instruction-aware open-weight encoders enclose an equal or even a larger amount of affective information in comparison with proprietary counterparts when evaluated at word level. In contrast, embeddings of task-tuned and proprietary encoders reach the highest scores on sentence-level affective classification. Furthermore, a qualitative analysis of latent representations and their encoded affective cues is provided.

Related papers

• LayerCake: Token-Aware Contrastive Decoding within Large Language Model Layers [53.43862310647276]
  Large language models (LLMs) excel at natural language understanding and generation but remain vulnerable to factual errors.<n>We introduce a token-aware, layer-localized contrastive decoding method that aligns specific token types with their most influential transformer layers to improve factual generation.<n>Our method requires no additional training or model modification, and experiments demonstrate that our method consistently improves factuality across multiple LLMs and various benchmarks.
  arXiv  Detail & Related papers  (2025-07-06T14:35:43Z)
• Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation [52.51005875755718]
  We focus on EEG-to-text decoding and address its hallucination issue through the lens of posterior collapse.<n>Acknowledging the underlying mismatch in information capacity between EEG and text, we reframe the decoding task as semantic summarization of core meanings.<n>Experiments on the public ZuCo dataset demonstrate that GLIM consistently generates fluent, EEG-grounded sentences.
  arXiv  Detail & Related papers  (2025-05-21T05:29:55Z)
• Empowering Sparse-Input Neural Radiance Fields with Dual-Level Semantic Guidance from Dense Novel Views [66.1245505423179]
  We show that rendered semantics can be treated as a more robust form of augmented data than rendered RGB.<n>Our method enhances NeRF's performance by incorporating guidance derived from the rendered semantics.
  arXiv  Detail & Related papers  (2025-03-04T03:13:44Z)
• Continuous Adversarial Text Representation Learning for Affective Recognition [1.319058156672392]
  We propose a novel framework for enhancing emotion-aware embeddings in transformer-based models.<n>Our approach introduces a continuous valence-arousal labeling system to guide contrastive learning.<n>We employ a dynamic token perturbation mechanism, using gradient-based saliency to focus on sentiment-relevant tokens, improving model sensitivity to emotional cues.
  arXiv  Detail & Related papers  (2025-02-28T00:29:09Z)
• Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models [23.670143829183104]
  The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. We propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
  arXiv  Detail & Related papers  (2023-11-06T21:34:49Z)
• Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization [76.57699934689468]
  We propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side to enhance the performance of neural models. To overcome the challenge of token-level retrieval in capturing contextual code semantics, we also propose integrating code semantics into individual summary tokens.
  arXiv  Detail & Related papers  (2023-05-18T16:02:04Z)
• REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection [3.6678641723285446]
  We propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM. We leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention.
  arXiv  Detail & Related papers  (2023-01-21T19:28:25Z)
• Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors [24.365876333182207]
  We propose a novel training loss based on per-utterance Dirichlet prior distributions for verbal emotion recognition. An additional metric is used to evaluate the performance by detecting test utterances with high labelling uncertainty. Experiments with the widely used IEMOCAP dataset demonstrate that the two-branch structure achieves state-of-the-art classification results.
  arXiv  Detail & Related papers  (2022-03-08T23:30:01Z)
• A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language [0.9749560288448115]
  We start with a rich word embedding pre-trained from a large general dataset, which is then augmented with embeddings learned from a much smaller and more specific domain dataset through a simple non-linear mapping mechanism. We show that our augmented word embedding representations achieve a significantly better F1 score than the others, specially when applied to a high quality dataset.
  arXiv  Detail & Related papers  (2021-06-24T07:15:09Z)
• Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation [81.55533657694016]
  We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three) We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
  arXiv  Detail & Related papers  (2020-11-02T08:07:50Z)
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [53.31911669146451]
  Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks. These graphs are usually incomplete, urging auto-completion of them. graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings. textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations.
  arXiv  Detail & Related papers  (2020-04-30T13:50:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.