Research map / Label-efficient and robust learning / Recognition and long-tail
Context-Sensitive Twitter Sentiment Classification Using Neural Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add all contextualized features (conversation, author, topic) (ablation) | local neural model · Twitter balanced | macro-F: 83.03 -> 92.27 | Helped |
| add topic-based context (ablation) | local neural model · Twitter balanced | macro-F: 83.03 -> 91.56 | Helped |
| add conversation-based context (ablation) | local neural model · Twitter balanced | macro-F: 83.03 -> 84.72 | Helped |
| add author-based context (ablation) | local neural model · Twitter unbalanced | macro-F: 80.90 -> 82.76 | Helped |
| context-based neural model vs SSWE | Twitter sentiment · Twitter unbalanced | macro-F: 80.68 -> 91.33 | Helped |
| separated vs shared word representations for content and context (ablation) | context-based model · Twitter | macro-F | Helped |
| add character features and pretrained embeddings (ablation) | local and context models · Twitter | macro-F | Helped |
| combine max, min and average pooling (ablation) | local neural model · Twitter | macro-F | Helped |
Closest papers by meaning
- Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings (AAAI 2016 · similarity 0.87)
- Gated Neural Networks for Targeted Sentiment Analysis (AAAI 2016 · similarity 0.86)
- How recurrent networks implement contextual processing in sentiment analysis (ICML 2020 · similarity 0.85)
- Learning Sentiment-Specific Word Embedding via Global Sentiment Representation (AAAI 2018 · similarity 0.85)
- Learning Latent Opinions for Aspect-level Sentiment Classification (AAAI 2018 · similarity 0.84)
- You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification (AAAI 2024 · similarity 0.81)
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