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Cited by 142Sentiment Analysis and Opinion MiningEmotion and Mood RecognitionMental Health via Writing

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

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Baziotis, Christos, Nikolaos, Athanasiou, Chronopoulou, Alexandra, Kolovou, Athanasia, Paraskevopoulos, Georgios, Ellinas, Nikolaos, Narayanan, Shrikanth, Potamianos, Alexandros·2018·Proceedings of The 12th International Workshop on Semantic Evaluation
DOI 10.18653/v1/s18-1037
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Abstract

Christos Baziotis, Athanasiou Nikolaos, Alexandra Chronopoulou, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Shrikanth Narayanan, Alexandros Potamianos. Proceedings of the 12th International Workshop on Semantic Evaluation. 2018.

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  • Most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions.

    “However, most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions [7], [9], [10].”

    From Emotion Classification With Visibility Graphs (2025) · cites this paper as [7]

    Section 4.2 (page unavailable) says certain words are highly indicative of particular emotions in the multi-label classification task, supporting the usefulness of emotion-specific cues. It does not say that most existing MLEC models fail to capture those associations; it reports that a bag-of-words baseline performs slightly better than a neural bag-of-words baseline on that task. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

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Authors
Baziotis, C., Nikolaos, A., Chronopoulou, A., Kolovou, A., Paraskevopoulos, G., Ellinas, N., Narayanan, S., & Potamianos, A.
Affiliations
  • National Technical University of Athens (GR)
  • University of Southern California (US)
ORCID
Venue
Proceedings of The 12th International Workshop on Semantic Evaluation
Publisher
Association for Computational Linguistics
Published
2018
Pages
245-255
DOI
10.18653/v1/s18-1037
OpenAlex
W2798132978
Cited by
142
References
41
Citations by year
2026: 6 · 2025: 8 · 2024: 10 · 2023: 22 · 2022: 25 · 2021: 22 · 2020: 22 · 2019: 21 · 2018: 6
FWCI
7.22
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • International Workshop on Semantic Evaluation (SemEval) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Nvidia
  • Horizon 2020 Framework Programme (687831, EU‐H2020)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords
tweets, transfer learning, sentiment analysis, social media text

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