Summary of Emotion Classification in Short English Texts Using Deep Learning Techniques, by Siddhanth Bhat
Emotion Classification in Short English Texts using Deep Learning Techniques
by Siddhanth Bhat
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new study explores deep learning techniques for detecting emotions in short English texts, introducing a novel dataset called “SmallEnglishEmotions” comprising 6372 annotated texts. Researchers employ transfer learning and word embedding models like BERT to achieve superior accuracy, outperforming alternative methods on the newly introduced dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Detecting emotions in short texts from under-resourced languages is a challenging problem that requires specialized tools and strategies. A new study looks at how well deep learning techniques can do this job. The researchers use a special kind of AI called BERT to help them detect emotions in short English texts. They also create a new dataset with 6372 texts that they’ve labeled with different emotions. By using these methods, the researchers are able to accurately categorize the texts and identify the emotions. |
Keywords
» Artificial intelligence » Bert » Deep learning » Embedding » Transfer learning
