mlsanityData to 14 Oct 2026

NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning

arXiv 1804.06658 ↗First posted 18 Apr 2018cs.CLcited by 23

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Abstract (arXiv)

In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self attention mechanism. The attention mechanism improves the model performance and allows us to identify salient words in tweets, as well as gain insight into the models making them more interpretable. Our model utilizes a set of word2vec word embeddings trained on a large collection of 550 million Twitter messages, augmented by a set of word affective features. Due to the limited amount of task-specific training data, we opted for a transfer learning approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A. The proposed approach ranked 1st in Subtask E "Multi-Label Emotion Classification", 2nd in Subtask A "Emotion Intensity Regression" and achieved competitive results in other subtasks.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2018.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 1; 2024: 1; 2025: 1; 2026: 2.

23 papers.

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  1. 2021SpanEmo: Casting Multi-label Emotion Classification as Span-predictioncited by 37
  2. 2022Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotioncited by 14
  3. 2021Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognitioncited by 10
  4. 2022An Effective Deployment of Contrastive Learning in Multi-label Text Classificationcited by 9
  5. 2022Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formatscited by 7
  6. 2021Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approachcited by 4
  7. 2021Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Trainingcited by 4
  8. 2021LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classificationcited by 3
  9. 2021Cross Domain Emotion Recognition using Few Shot Knowledge Transfercited by 2
  10. 2024Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settingscited by 1
  11. 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysiscited by 0
  12. 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Searchcited by 0
11 more
  1. 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
  2. 2022CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lecturescited by 0
  3. 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
  4. 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
  5. 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
  6. 2022Using Graph Neural Networks for Program Terminationcited by 0
  7. 2022VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social Mediacited by 0
  8. 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
  9. 2025Sentiment Analysis in SemEval: A Review of Sentiment Identification Approachescited by 0
  10. 2026SyntaxMind at BLP-2025 Task 1: Leveraging Attention Fusion of CNN and GRU for Hate Speech Detectioncited by 0
  11. 2026NTDH: Complex Reasoning for Comprehensive Affective Analysiscited by 0