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awesome-asc's Introduction

Awesome for Sentiment Analysis

Tutorail

  • Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision and Challenges (IEEE Access 2019). Paper

  • Deep Learning for Sentiment Analysis : A Survey (2018). Paper

Aspect/Target Extraction

  • Sentiment Tagging with Partial Labels using Modular Architectures (ACL 2019). Paper

  • An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (ACL 2019). Paper

  • DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (ACL 2019). Paper

  • Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (ACL 2019). Paper

  • Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (ACL 2019). Paper

  • Aspect Based Sentiment Analysis with Gated Convolutional Networks (ACL 2018). Paper, Code

  • Transformation Networks for Target-Oriented Sentiment Classification (ACL 2018). Paper, Code

  • Target-Sensitive Memory Networks for Aspect Sentiment Classification (ACL 2018). Paper, Code

    • Six methods for modeling target-context sentiment interaction.

    • Decomple different functioning components with different representations, for example, the same set of vector representations is used for multiple purposes (to learn context representations and capture the interplay between contexts and aspects).

  • Recurrent Attention Network on Memory for Aspect Sentiment Analysis (EMNLP 2017). Paper

Aspect/Target Sentiment Classification

  • Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (ACL 2019). Paper

Joint Aspect/Target Eatraction and Classification

  • A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (AAAI 2019). Paper

Emotion-Cause Pair Extraction

  • Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (ACL 2019). Paper

Sentence-/Document-level Sentiment Classification

  • Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (EMNLP 2015). Paper

Data

To be continued ...

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