Multi-task Learning & End-to-End

• [paper - jin/ARTS_TestSet) -**ARTS** (Challenge set for SemEval14)
• [paper - ***TOWE***
• [paper - for-RRC-ABSA) - ***Restaurants14 & Laptop14***
• [paper - jin/ARTS_TestSet) -**ARTS** (Challenge set for SemEval14)
• [paper - PyTorch/tree/master/datasets/acl-14-short-data) - ***Twitter for ATSC***
• [paper - mitchell.com/code/index.html)
• [paper
• [paper
• [paper - ***LSTM-LOC***
• [paper - nlp/MAMS-for-ABSA) - ***MAMS***

Normal Sentiment Analysis Dataset (Coarse-grained)

• [paper

• [paper - ***BERT-SPC***
• [paper - PyTorch) - ***PBAN***
• [paper - ***DAuM***
• [paper - ***HUARN***
• [paper
• [paper - inter-aspect)
• [paper - memory-update-entnet)
• [paper - ***AOA***
• [paper
• [paper
• [paper - ***MGAN***
• [paper - ***PCNN***
• [paper - ***LCR-Rot***
• [paper - ***PWCN***
• [paper - ***ASGCN***
• [paper - ***CDT-ABSA***
• [paper - ***AGDT***
• [paper - ***CAN***
• [paper - for-RRC-ABSA) - ***BERT-PT***
• [paper - BERT-pair)
• [paper - PyTorch/blob/master/models/aen.py) - ***AEN-BERT***
• [paper - ABSA) - ***LCF-BERT***
• [paper - ATEPC) - ***LCF-ATEPC***
• [paper - Pan/ABSA-PyTorch/blob/master/models/td_bert.py) - ***TD-BERT***
• [paper - adapted-atsc) - ***BERT-ADA***
• [paper
• [paper - BERT) - ***LCFS-BERT***
• [paper
• [paper - ***ATN***
• [paper - ***AOA***
• [paper - ***AGDT***
• [paper - PyTorch/blob/master/models/aen.py) - ***AEN-BERT***
• [paper - ***InterGCN***
• [paper - ***SAGAT***
• [paper
• [paper - ***TD-LSTM TC-LSTM***
• [paper - ***H-LSTM***
• [paper - ***MemNet***
• [paper - ***IAN***
• [paper - ***RAM***
• [paper
• [paper - ***BiLSTM-ATT-G***
• [paper - ***GCAE***
• [paper - ***TMN***
• [paper - ***TNet***
• [paper - level-sentiment)
• [paper - ***AF-LSTM***
• [paper - lstm) - ***Sentic LSTM***
• [paper - ***BiLSTM-ATT-G***
• [paper - ***HUARN***
• [paper - memory-update-entnet)
• [paper - ***PCNN***
• [paper - ***ATN***
• [paper - ***InterGCN***
• [paper - ***ATAE-LSTM***
• [paper - ***MGAN***
• [paper - BERT-pair)
• [paper - ***CAN***
• [paper - ***H-LSTM***
• [paper - ***Cabasc***

Multi-task Learning & End-to-End

• [paper - ***MTNA***
• [paper - ***MGAN***
• [paper - TBSA) - ***UNIFIED E2E-TBSA***
• [paper - E2E-ABSA) - ***IMN-E2E-ABSA***
• [paper - ***EI***
• [paper - E2E-ABSA) - ***BERT-E2E-ABSA***
• [paper - E2E-ABSA) - ***Transferable-E2E-ABSA***
• [paper - Triplet-data) - ***ASTE***
• [paper - ***SpanABSA***
• [paper - WHU/RACL) - ***RACL***
• [paper
• [paper
• [paper - ***D-GCN***
• [paper
• [paper - IIPLab/SDRN) - ***AOPE SDRN***
• [paper
• [paper
• [paper
• [paper - 2D)
• [paper - Emotion-Cause)
• [paper - HITSZ/TransECPE)
• [paper
• [paper - ***D-GCN***
• [paper

Normal Sentiment Analysis Dataset (Coarse-grained)

• songyouwei / ABSA-PyTorch - Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析，使用PyTorch实现
• AlexYangLi / ABSA_Keras - Keras Implementation of Aspect based Sentiment Analysis
• NUSTM / ABSC - aspect-based sentiment classification
• jiangqn / Aspect-Based-Sentiment-Analysis - A paper list for aspect based sentiment analysis.
• haiker2011 / awesome-nlp-sentiment-analysis - 收集NLP领域相关的数据集、论文、开源实现，尤其是情感分析、情绪原因识别、评价对象和评价词抽取方面

Normal Sentiment Analysis Dataset (Coarse-grained)

• [slide
• [paper
• [paper
• [paper

Normal Sentiment Analysis Dataset (Coarse-grained)

• [paper
• [paper
• [paper

Chinese

• 阿里巴巴 / 细粒度情感分析任务（ABSA）的最新进展
• Kaiyuan Gao / 基于特定实体的文本情感分类总结 [PART I
• NJU / 如何理解用户评论中的细粒度情感？面向目标的观点词抽取
• 平安寿险PAI / 细粒度情感分析在保险行业的应用