mlsanityData to 14 Oct 2026

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

arXiv 1711.09645 ↗First posted 27 Nov 2017cs.CL cs.IR cs.LGcited by 23

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Abstract (arXiv)

We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate superior performance against multiple baselines, whereas a judgement elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2017.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 3; 2025: 3; 2026: 1.

23 papers.

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  1. 2021Aspect-Controllable Opinion Summarizationcited by 47
  2. 2021Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similaritycited by 35
  3. 2022Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalizationcited by 17
  4. 2021SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classificationcited by 10
  5. 2023WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdomcited by 6
  6. 2025LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Informationcited by 6
  7. 2021A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysiscited by 4
  8. 2023A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classificationcited by 4
  9. 2022Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequencescited by 2
  10. 2024A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysiscited by 2
  11. 2021W-RST: Towards a Weighted RST-style Discourse Frameworkcited by 1
  12. 2021Preliminary Steps Towards Federated Sentiment Classificationcited by 1
11 more
  1. 2023A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysiscited by 1
  2. 2024Text Classification using Graph Convolutional Networks: A Comprehensive Surveycited by 1
  3. 2022Comparative Snippet Generationcited by 0
  4. 2022Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial Newscited by 0
  5. 2022Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularizationcited by 0
  6. 2022Large Discourse Treebanks from Scalable Distant Supervisioncited by 0
  7. 2023Summative Student Course Review Tool Based on Machine Learning Sentiment Analysis to Enhance Life Science Feedback Efficacycited by 0
  8. 2024FRACTAL: Fine-Grained Scoring from Aggregate Text Labelscited by 0
  9. 2025Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learningcited by 0
  10. 2025milearn: A Python Package for Multi-Instance Machine Learningcited by 0
  11. 2026PREFER: Personalized Review Summarization with Online Preference Learningcited by 0