Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Generating Reviews Discovering Sentiment | 1,361 | 4 years ago | 28 | mit | Python | |||||
Code for "Learning to Generate Reviews and Discovering Sentiment" | ||||||||||
Awesome Sentiment Analysis | 770 | 5 years ago | cc-by-sa-4.0 | |||||||
😀😄😂😭 A curated list of Sentiment Analysis methods, implementations and misc. 😥😟😱😤 | ||||||||||
Aspect Based Sentiment Analysis | 288 | 2 years ago | mit | |||||||
A paper list for aspect based sentiment analysis. | ||||||||||
Awesome Nlp Sentiment Analysis | 275 | 3 years ago | gpl-3.0 | |||||||
:book: 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。 | ||||||||||
Absapapers | 268 | a year ago | 2 | |||||||
Worth-reading papers and related awesome resources on aspect-based sentiment analysis (ABSA). 值得一读的方面级情感分析论文与相关资源集合 | ||||||||||
Chinese_conversation_sentiment | 190 | 7 years ago | 4 | |||||||
A Chinese sentiment dataset may be useful for sentiment analysis. | ||||||||||
Socialsent | 171 | 2 years ago | 3 | March 07, 2017 | 11 | apache-2.0 | Python | |||
Code and data for inducing domain-specific sentiment lexicons. | ||||||||||
Sa Papers | 108 | 5 years ago | ||||||||
📄 Deep Learning 中 Sentiment Analysis 論文統整與分析 😀😡☹️😭🙄🤢 | ||||||||||
Contextual Utterance Level Multimodal Sentiment Analysis | 97 | 2 years ago | 1 | Python | ||||||
Context-Dependent Sentiment Analysis in User-Generated Videos | ||||||||||
Vista Net | 82 | 2 months ago | 4 | mit | Python | |||||
Code for the paper "VistaNet: Visual Aspect Attention Network for Multimodal Sentiment Analysis", AAAI'19 |
Worth-reading papers and related awesome resources on aspect-based sentiment analysis (ABSA). This repository mainly focused on aspect-term sentiment classification (ATSC). ABSA task contains five fine-grained subtasks:
Suggestions about adding papers, repositories and other resource are welcomed!
值得一读的方面级情感分析论文与相关资源集合。这里主要关注方面词(aspect-term)的情感分类。具体来说,方面级情感分析包括方面词情感分类、方面词抽取、方面类目情感分类、方面类目抽取、观点词抽取五个子任务。
欢迎新增论文、代码仓库与其他资源等建议!
We will add a score table of representative and latest ABSA models like NLP-progress in the near future, so stay tuned!
近期将参考NLP-progress的形式增加一个数据集分值表,敬请期待!
Combining two or more ABSA's subtasks in one framework to produce results is an intutively effective way for industrial application. There are three patterns of multi-task learning: pipeline, joint and end-to-end model. For pipeline pattern, the framework complete subtasks in more than one step, using the result of last step to guide the next step's output, which might lead to error propogation problem. Joint model processes the data with shared layers to extract universal semantic features for all subtasks. Then model outputs results of different tasks through task-specific layers. End-to-end model complete tasks like sequence labeling.