PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
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Abstract (arXiv)
This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level representations of emotion inside texts through large distantly labeled corpora with emojis and hashtags. We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR) and logistic regression to solve the competition tasks. Our system is placed among the Top3 for all subtasks we participated.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 23 Apr 2018.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2022CEFER: A Four Facets Framework based on Context and Emotion embedded features for Implicit and Explicit Emotion Recognitioncited by 0
- 2024Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Trainingcited by 0
- 2025Sentiment Analysis in SemEval: A Review of Sentiment Identification Approachescited by 0
- 2025Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches13 Mar 2025
- 2024Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training22 Sep 2024
- 2022CEFER: A Four Facets Framework based on Context and Emotion embedded features for Implicit and Explicit Emotion Recognition28 Sep 2022