Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data

Schoene, Annika Marie

doi:10.1609/AAAI.V34I10.7138

Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data

Annika Marie Schoene

AAAI 2020 pp. 13732-13733

doi:10.1609/AAAI.V34I10.7138 /aaai/2020/schoene2020aaai-hybrid/

Abstract

This paper states the challenges in fine-grained target-dependent Sentiment Analysis for social media data using recurrent neural networks. First, the problem statement is outlined and an overview of related work in the area is given. Then a summary of progress and results achieved to date and a research plan and future directions of this work are given.

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Text

Schoene. "Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7138

Markdown

[Schoene. "Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/schoene2020aaai-hybrid/) doi:10.1609/AAAI.V34I10.7138

BibTeX

@inproceedings{schoene2020aaai-hybrid,
  title     = {{Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data}},
  author    = {Schoene, Annika Marie},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13732-13733},
  doi       = {10.1609/AAAI.V34I10.7138},
  url       = {https://mlanthology.org/aaai/2020/schoene2020aaai-hybrid/}
}