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https://github.com/nicolay-r/awesome-sentiment-attitude-extraction

A curated list of awesome sentiment analysis studies, in which attitude corresponds to the text position conveyed by Subject towards other Object mentioned in text such as: entities, events, etc.
https://github.com/nicolay-r/awesome-sentiment-attitude-extraction
List: awesome-sentiment-attitude-extraction
aaai awesome awesome-list chatgpt deep-learning emnlp language-model low-resource-nlp machine-learning naacl natural-language-processing nips nlp relation-classification sentiment-analysis sentiment-attitude-extraction stance-detection state-of-the-art trends
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A curated list of awesome sentiment analysis studies, in which attitude corresponds to the text position conveyed by Subject towards other Object mentioned in text such as: entities, events, etc.
Host: GitHub
URL: https://github.com/nicolay-r/awesome-sentiment-attitude-extraction
Owner: nicolay-r
Created: 2021-09-22T14:14:26.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-12-14T11:56:37.000Z (2 months ago)
Last Synced: 2024-12-14T12:29:38.261Z (2 months ago)
Topics: aaai, awesome, awesome-list, chatgpt, deep-learning, emnlp, language-model, low-resource-nlp, machine-learning, naacl, natural-language-processing, nips, nlp, relation-classification, sentiment-analysis, sentiment-attitude-extraction, stance-detection, state-of-the-art, trends
Homepage: http://nlpprogress.com/russian/sentiment-analysis.html
Size: 1.42 MB
Stars: 18
Watchers: 3
Forks: 1
Open Issues: 2
Metadata Files:
- Readme: README.md
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README

        # Awesome Sentiment Attitude Extraction

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



    



A curated list of awesome studes related to sentiment attitude extraction,

in which **attitude** corresponds to the *text position* 

conveyed by Subject towards other Object

mentioned in text such as: entities, events, etc.

 

This repository collects works both related to **relation extraction** and **sentiment analysis** 

in which these two domains are inextricably linked, including event factualization as fundamentional studies 

for sentiment inference, stance detection.

Contributing: Please feel free to make *pull requests* or contact me [[contacts]](https://nicolay-r.github.io/)

## Contents

* [Related Studies](#related-studies)

* [Frameworks](#frameworks)

* [Annotation Schemas](#annotation-schemas)

* [Papers](#papers)

    * [Large Language Models](#large-language-models)

      * [Reasoning](#reasoning)

      * [Fact-Checking Adaptation](#fact-checking-adaptation)

      * [Chain-of-Thought](#chain-of-thought)

      * [Conversational Systems](#conversational-systems)

    * [Language Models](#language-models)

    	* [Graph-Based](#graph-based)

    	* [Low Resource Tunings](#low-resource-tunings)

    	* [Prompts and Knowledge Examination](#prompts-and-knowledge-examination)

        * [Architectures](#architectures) 

    * [Conventional neural-network based Models](#conventional-neural-network-based-models)

    * [Conventional Machine Learning Models](#conventional-machine-learning-models)

    * [CRF-based Models](#crf-based-models)

    * [Rule-based Verb-applicable Models](#rule-based-verb-applicable-models)

    * [Subsidiary Studies And Resources](#subsidiary-studies-and-resources)

    * [Miscellaneous](#miscellaneous)

* [Thesises](#thesises)

* [Datasets](#datasets)

## Related studies

* [Natural Language Processing](https://github.com/keon/awesome-nlp#nlp-in-chinese)

    * [Sentiment Analysis](https://github.com/laugustyniak/awesome-sentiment-analysis)

        * [Targeted Setiment Analysis](https://arxiv.org/pdf/1905.03423.pdf)

        * [Structured Sentiment Analysis](https://aclanthology.org/2022.semeval-1.180.pdf) (SemEval Task 10)

        * [Aspect-based Sentiment Analysis](https://github.com/jiangqn/Aspect-Based-Sentiment-Analysis)

        * [Hate-speech detection](https://aclanthology.org/W17-1101.pdf)

    * [Relation Extraction](https://github.com/roomylee/awesome-relation-extraction)

    * [Stance Detection](https://github.com/sumeetkr/AwesomeStanceLearning)

  

## Frameworks

* **bulk-chain** [[github]](https://github.com/nicolay-r/bulk-chain) 

    * Framework that exploits Chain-of-Thought concept and provides minimalistic solution for zero-shot inferences. For example, you can exploit the concept of `aspect-opininon-reason` chain from  [THOR-ISA](https://github.com/scofield7419/THOR-ISA) to adapt it for attitude extraction.

* **FaiMA** [[github]](https://github.com/SupritYoung/FaiMA)

    * Framework that integrates graph-based models and linguistics, with a core feature aimed at in-context learning for multi-domain SA.

* **Reasoning-for-Sentiment-Analysis-Framework** [[github]](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework)

    * This frameworks repesent a reforget 🛠️ version of the `THOR-ISA` framework: 

    * **THOR-ISA** [[github]](https://github.com/scofield7419/THOR-ISA)

        * Propt-based framework for setiment Analysis that based on Chain-of-Though concept for obtaining the result sentiment class out of the LLM system.	

* **OpenPrompt** [[github]](https://github.com/thunlp/OpenPrompt)

    * Enhanced tool for automatic completion of the prompt via the provided resources.	

* **ChatGPT** [[site]](https://openai.com/blog/chatgpt/)

    * Conversation system that is trained to follow the instruction in a prompt and provide a detailed response; 

      examples on how it could be adapted reviewed in the following [work](https://arxiv.org/pdf/2212.14548.pdf).

* **arekit-prompt-sampler** 

  [[github]](https://github.com/nicolay-r/arekit-prompt-sampler) 

  [[prompt-engeneering-guide]](https://github.com/dair-ai/Prompt-Engineering-Guide)

    * Sentiment Attitude Extraction sources sampling with language 

    transferring and prompting API for further ChatGPT-alike model requests, powered by [AREkit](https://github.com/nicolay-r/AREkit).

* **ARElight** [[github]](https://github.com/nicolay-r/ARElight)

    * [AREkit-based](https://github.com/nicolay-r/AREkit) application for a granular view onto sentiments between entities in a mass-media texts written in Russian

* **AREnets** [[github]](https://github.com/nicolay-r/AREnets)

    * Is an OpenNRE like project, but the kernel based on tensorflow library, with implementation of neural networks on top of it, designed for Attitude and Relation Extraction tasks.

* **AREkit** [[github]](https://github.com/nicolay-r/AREkit) [[research-applicable-paper]](https://arxiv.org/pdf/2006.13730.pdf)

    * Is an open-source and extensible toolkit focused on data preparation for 

    document-level relation extraction organization. 

     It complements the OpenNRE functionality, as in terms of the latter, 

     document-level RE setting is not widely explored (2.4 [[paper]](https://aclanthology.org/D19-3029.pdf)). 

* **DeRE**

 [[github]](https://github.com/ims-tcl/DeRE) 

 [[paper]](https://aclanthology.org/D18-2008/)

     * Is an open-source framework for **de**claritive **r**elation **e**xtraction, and therefore allows to declare your own task (using XML schemas) and apply manually implemented models towards it (using a provided API).

* **OpenNRE**

 [[github]](https://github.com/thunlp/OpenNRE) 

 [[paper]](https://aclanthology.org/D19-3029.pdf)

    * Is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE) between named entities.

* **DeepPavlov-0.17.0** 

[[docs]](http://docs.deeppavlov.ai/en/0.17.0/features/models/re.html) 

[[post]](https://medium.com/deeppavlov/relation-extraction-for-deeppavlov-library-d1f7b57365b3)

    * Is an entire relation extraction component for DeepPavlov opensource library, proposed by Anastasiia Sedova.

* Others ... [[awesome-relation-extraction]](https://github.com/roomylee/awesome-relation-extraction/blob/master/README.md#frameworks)

[Back to Top](#contents)

## Annotation Schemas

* **OpinionML** [[paper]](https://www.researchgate.net/publication/332423185_OpinionML-Opinion_Markup_Language_for_Sentiment_Representation)

* **SentiML** [[paper]](https://dl.acm.org/doi/10.1145/2517978.2517994)

* **OpinionMiningML** [[paper]](https://d1wqtxts1xzle7.cloudfront.net/47692116/OpinionMining-ML20160801-28120-mzgsge-libre.pdf?1470049517=&response-content-disposition=inline%3B+filename%3DOpinionMining_ML.pdf&Expires=1667567660&Signature=L~lOd1CoiQGRU8X28xfKiEJbXXThItxUEpOx9uSS62nUhP9MBaR-1-XCVnKk1brFLUq5X1ooMkj0MCdGdnEPHwl7mLJLFmMbko9od207~EYvsbPyvPl9N6R9ceQMj3wH-W2A6EEigBZ8hTPxbAV6HWPOgFzIPOlyBS20-0o6SMTdtEFny714EtoVfS-E941qliBJyHdcOYVzT-uf4MHrceBHhKvfpwe0xDdLDC4QLVbbYbfDuWgbak1QEm7RKwQEITGeYE8zK5~1YIJT~MPvlP7aSbyPOjAfMpXbh2QCkBJC2KSY9q19pQOQz4uGtWsXQFbSRSLFxDFCK00ynuBccw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)

* **EmotionML** [[paper]](https://www.researchgate.net/publication/221622141_EmotionML_-_An_Upcoming_Standard_for_Representing_Emotions_and_Related_States)

[Back to Top](#contents)

## Papers

	

[Back to Top](#contents)

### Large Language Models

> [Awesome-LLM list](https://github.com/Hannibal046/Awesome-LLM)

#### Reasoning

* Reverse Thinking Makes LLMs Stronger Reasoners [[paper]](https://arxiv.org/pdf/2411.19865.pdf) [[X/twitter]](https://x.com/cyjustinchen/status/1863636652264149212)

    * `Concept: Using referse thinking in sentiment analysis by treating this problem as NLI through the explanations. Note requires explanations like for example` [RuOpinionNE-2024](https://github.com/dialogue-evaluation/RuOpinionNE-2024)

    * Justin Chih-Yao Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister

    * **ArXiV Pre-print 2024**

* Stream of Search (SoS): Learning to Search in Language [[paper]](https://arxiv.org/pdf/2404.03683) [[review]](https://openreview.net/forum?id=2cop2jmQVL#discussion) [[X/Twitter]](https://x.com/_philschmid/status/1847565606964646077?t=a75iyBMH2m8vZsAbM6-Z6Q&s=19)

    * `Concept: language models can learn to search in language, autonomously using and discovering new search strategies to solve problems.` from here [[review]](https://openreview.net/forum?id=2cop2jmQVL#discussion)

    * Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, Noah D. Goodman

    * **COLM 2024** (Published: 10th July)

* STaR: Bootstrapping Reasoning With Reasoning [[paper]](https://arxiv.org/pdf/2203.14465) [[X/Twitter]](https://x.com/_philschmid/status/1847908923203825709?t=PhyQUdNwIPdoa9xGCjxKbA&s=19) [[review]](https://openreview.net/forum?id=_3ELRdg2sgI)

    * `Concept: STaR allows a language model's "chain-of-thought" rationale generation ability to be bootstrapped from a few initial few-shot rationales on datasets without rationales.` from here [[review]](https://openreview.net/forum?id=_3ELRdg2sgI)

    * Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman

    * **NerIPS 2022**

[Back to Top](#contents)

#### Fact-Checking Adaptation

> **NOTE:** Requires / Assumes the presence of factual knowledgebase

* Consistent Document-Level Relation Extraction via Counterfactuals [[paper]](https://www.arxiv.org/abs/2407.06699) [[code]](https://github.com/amodaresi/CovEReD)

    * `Concept: use factual relations for fictional context construction and LLM validation`

    * Ali Modarressi, Abdullatif Köksal, Hinrich Schütze

    * **EMNLP-2024, 15th of October 2024**

* Learning to Refine with Fine-Grained Natural Language Feedback [[paper]](https://aclanthology.org/2024.findings-emnlp.716.pdf) [[code]](https://github.com/ManyaWadhwa/DCR)

    * `Concept: When treating attitudes as facts, we can adopt zero-shot LLM-based fact cheking as: Detect-Critique-Refine`

    * Manya Wadhwa, Xinyu Zhao, Junyi Jessy, Li Greg Durrett

    * **EMNLP-2024**

* Zero-Shot Fact Verification via Natural Logic and Large Language Models [[paper]](https://aclanthology.org/2024.findings-emnlp.991.pdf) [[code]](https://github.com/marekstrong/Zero-NatVer)

    * `Concept: Use natural logic  for proving the fact of attitude presence in a zero-shot learning mode (see code)`

    * **EMNLP-2024**

#### Chain-of-Thought

* [FaiMA](https://github.com/SupritYoung/FaiMA): Feature-aware In-context Learning for Multi-domain

Aspect-based Sentiment Analysis 

[[paper]](https://arxiv.org/pdf/2403.01063.pdf)

[[code]](https://github.com/SupritYoung/FaiMA)

    * [Framework](https://github.com/SupritYoung/FaiMA) `that integrates` [graph-based models](#graph-based)` and lingustics, with core feature aimed at in-context-learning feature for multi-domain SA; The framework is designed for multidomain datasets; 

      Due to graphs and pairs-generation module, it may find major contribution in **attitude-based** sentiment extraction and target-oriented SA.`

    * Songhua Yang, Xinke Jiang, Hanjie Zhao, Wenxuan Zeng, Hongde Liu, Yuxiang Jia

    * **LREC-COLING 2024, Long Paper**; Submitted 2 Mar. 2024.

* Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations

  [[paper]](https://arxiv.org/abs/2312.10961)

  [[harvard-paper]](https://ui.adsabs.harvard.edu/abs/2023arXiv231210961O/abstract)

    * `integrates explicit sentiment augmentations, acted as <> that augment LLM input context`

    * Jihong Ouyang, Zhiyao Yang, Silong Liang, Bing Wang, Yimeng Wang, Ximing Li

    * Arxiv Pre-print, submitted: 18 Dec. 2024

* Sentiment Analysis through LLM Negotiations 

 [[paper]](https://arxiv.org/abs/2311.01876) 

 [[open-review]](https://openreview.net/pdf?id=1VlIXyAw04k)

    * `generator-discriminator of negotiating the result label`

    * Xiaofei Sun, Xiaoya Li, Shengyu Zhang, Shuhe Wang, Fei Wu, Jiwei Li, Tianwei Zhang, Guoyin Wang

    * Arxiv Pre-print, submitted: 2024

* Reasoning Implicit Sentiment with Chain-of-Thought Prompting [[paper]](https://aclanthology.org/2023.acl-short.101.pdf) [[code]](https://github.com/scofield7419/THOR-ISA)

	* `Sequence of 3 prompts for conversational system, complemented by tge system responses. Reason is to cope with hallucination` [similar-studies](https://openreview.net/pdf?id=1PL1NIMMrw)  	

	* Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, Tat-Seng Chua

	* **ACL 2023, Short Papers**

[Back to Top](#contents)

#### Conversational Systems

  > Using [Language Models](#language-models) (usually LARGE-sized) in a combination with promts/questions 

   * Sentiment Analysis in the Era of Large Language Models: A Reality Check

    [[paper]](https://arxiv.org/pdf/2305.15005.pdf)

        * `application of the LLM and based on the latter ChatGPT for the variety set of sentiment analysis problems`

        * Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, Lidong Bing 

        * arXiv, 24 May 2023

  * Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech 

  [[paper]](https://arxiv.org/pdf/2302.07736.pdf)

	* Huang Fan, Kwak Haewoon, An Jisun

	* Harvard, Februrary, 2023

  * How would Stance Detection Techniques Evolve after the Launch of ChatGPT?

  [[paper]](https://arxiv.org/pdf/2212.14548.pdf)

  	* `Introducing prompt templater which allows to reach state-of-the art with zero-shot learning!`

  	* Bowen Zhang, Daijun Ding, Liwen Jing

	* Harvard, December, 2022

[Back to Top](#contents)

### Language Models

> [Awesome-LLM list](https://github.com/Hannibal046/Awesome-LLM)

#### Graph-Based

  * Comparing `Graph-` and `Seq2Seq-` based Models Highlights Difficulty in Structured Sentiment Analysis

  [[paper]](https://aclanthology.org/2022.semeval-1.188.pdf)

  [[code]](https://github.com/hitachi-nlp/graph_parser)

  	* Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, and Yasuhiro Sogawa

	* ACL-Workshop, 2022

  * Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph

  [[paper]](https://aclanthology.org/2021.findings-acl.278.pdf)

  	* Rui Liu, Zheng Lin, Yutong Tan1, Weiping Wang

	* ACL-IJCNLP 2021

	

[Back to Top](#contents)

#### Low Resource Tunings

  * Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon

    [[paper]](https://arxiv.org/pdf/2402.02113.pdf)

    [[code]](https://github.com/fajri91/ZeroShotMultilingualSentiment)

    * Fajri Koto, Tilman Beck, Zeerak Talat, Iryna Gurevych, Timothy Baldwin

    * NAACL-2024

  * Black-Box Tuning for Language-Model-as-a-Service

    [[paper]](https://arxiv.org/pdf/2201.03514.pdf)

    [[code]](https://github.com/txsun1997/Black-Box-Tuning)

    * `Non gradient p-tunes, wrapped in API in order to consider large Pre-Trained models (PTMs) adoptation as Service models`

    * Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu

    * Arxiv Pre-print, 2022

  * P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks 

  	[[paper]](https://arxiv.org/pdf/2110.07602.pdf)

	[[code]](https://github.com/THUDM/P-tuning-v2)

  	* `Proceeds Prefix-Tuning idea onto multiple layers of LM-model`

	* Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, Jie Tang

	* Dblp Jornal, 2021

  * The Power of Scale for Parameter-Efficient Prompt Tuning 

	[[paper]](https://aclanthology.org/2021.emnlp-main.243.pdf)

	[[code]](https://github.com/google-research/prompt-tuning)

	* `Prompt-designing, prompt-tuning comparison studies`

	* Brian Lester, Rami Al-Rfou, Noah Constant

	* EMNLP-2021

  * GPT Understands, Too 

        [[paper]](https://arxiv.org/pdf/2103.10385.pdf) 

        [[code]](https://github.com/THUDM/P-tuning)

	* `Promt Tuning (p-tuning), i.e. training only promt token embeddings before and after input sequence (x)`

	* Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang

	* 2021

  * Prefix-Tuning: Optimizing Continuous Prompts for Generation 

	[[paper]](https://aclanthology.org/2021.acl-long.353.pdf)

	[[code]](https://github.com/XiangLi1999/PrefixTuning)

	* `Training token prefixes for downstream tasks with frozen LM parameters` 

	* Xiang Lisa Li, Percy Liang

	* ACL/IJCNLP-2021

  * Language Models are Few-Shot Learners 

	[[paper]](https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf)

	* `Prompt designing. FS, 1S by presenting context as "[input,result] x k-times", where k > 1 (FewShot), k = 1 (OneShot); ZeroShot includes only descriptor of expected result`

	* Tom B. Brown, et. al.

	* NeurIPS-2020

  * AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts 

   	[[paper]](https://aclanthology.org/2020.emnlp-main.346.pdf)

	[[code]](https://github.com/ucinlp/autoprompt)

	* `Considering sentiment analysis task as MLM by predicting [MASK]; prompting input (x) with tokens (p1...pk), selected by gradient search (considering that label has corresponding tokens (prompts))`

	* Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh

	* EMNLP-2020

[Back to Top](#contents)

#### Prompts and Knowledge Examination

  * Sentiment Analysis in the Era of Large Language Models: A Reality Check

    [[paper]](https://arxiv.org/pdf/2305.15005.pdf)

    * `duplicated from the one in conversational systems section`

    * Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, Lidong Bing

    * arXiv, 24 May 2023

  * How Can We Know What Language Models Know? 

    [[paper]](https://aclanthology.org/2020.tacl-1.28.pdf) 

    [[code]](https://github.com/jzbjyb/LPAQA)

	* `Implemented model LPAQA: Language model Prompt And Query Archive`

	* Zhengbao Jiang, Frank F. Xu, Jun Araki, Graham Neubig

	* TACL-2020

  * Language Models as Knowledge Bases? 

    [[paper]](https://aclanthology.org/D19-1250.pdf)

    [[code]](https://github.com/facebookresearch/LAMA)

  	* Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel

	* EMNLP-2019	

  * Utilizing BERT for Aspect-Based Sentiment Analysis

via Constructing Auxiliary Sentence 

    [[paper]](https://aclanthology.org/N19-1035.pdf) 

    [[code]](https://github.com/HSLCY/ABSA-BERT-pair)

	* `Adopting a predefined prompt (QA/NLI formats) as a TextB input part`

	* Chi Sun, Luyao Huang, Xipeng Qiu

	* NAACL-HLT 2019

[Back to Top](#contents)

#### Architectures

  * BERT-based models (Encoder Reprsentation From Transorfmers) 

  [[papers]](https://github.com/roomylee/awesome-relation-extraction#encoder-representation-from-transformer)

    * `Considering BERT model as classifier`

    * Joohong Lee, Awesome Relation Extraction  

  * GPT-based (Encoder Reprsentation From Transorfmers) 

  [[papers]](https://github.com/roomylee/awesome-relation-extraction#decoder-representation-from-transformer)

    * `Considering GPT model competed for classification task`

    * Joohong Lee, Awesome Relation Extraction  

  * Comparing `Graph-` and `Seq2Seq-` based Models Highlights Difficulty in Structured Sentiment Analysis

  [[paper]](https://aclanthology.org/2022.semeval-1.188.pdf)

  [[code]](https://github.com/hitachi-nlp/graph_parser)

  	* `T5 and mT5 finetunnning`, i.e. 

	[Text-To-Text Transfer Transoformer](https://github.com/google-research/text-to-text-transfer-transformer) application

  	* Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, and Yasuhiro Sogawa

	* ACL-Workshop, 2022

    

[Back to Top](#contents)

### Conventional Neural-network based Models 

In this section we consider neural-network models based on convolutional, recurrent, recursive architectures.

* No Permanent Friends or Enemies: Tracking Relationships between Nations from News

   [[paper]](https://arxiv.org/pdf/1904.08950)

    * Xiaochuang Han, Eunsol Choi, Chenhao Tan

    * NAACL-HLT 2019

* Neural networks for open domain targeted sentiment

    [[paper]](https://aclanthology.org/D15-1073.pdf)

    * Meishan Zhang, Yue Zhang, Duy-Tin Vo

    * ACL 2015

       

[Back to Top](#contents)

### Conventional Machine Learning Models

* Document-level Sentiment Inference with Social, Faction, and Discourse Context 

[[paper]](https://aclanthology.org/P16-1032.pdf)

	* Eunsol Choi, Hannah Rashkin, Luke Zettlemoyer, Yejin Choi

	* ACL-2016

* Sentiment Analysis: Capturing Favorability Using Natural Language Processing [[paper]](https://dl.acm.org/doi/pdf/10.1145/945645.945658)

	* `it is originally called favorability analysis, semantic establishment between sentiment and subject`

	* Tetsuya Nasukawa, Jeonghee Yi 

	* K-CAP-2003 (ACM)

	

### CRF-based Models

* Open Domain Targeted Sentiment 

    [[paper]](https://aclanthology.org/D13-1171.pdf)

    * Margaret Mitchell, Jacqueline Aguilar, Theresa Wilson, Benjamin Van Durme

    * ACL 2013

### Rule-based Verb-applicable Models

* Stance detection in Facebook posts of a German right-wing party 

    [[paper]](https://aclanthology.org/W17-0904.pdf)

    * Manfred Klenner, Don Tuggener, Simon Clematide

    * `Verb-usages form`

    * ACL 2017 (2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics)

* An object-oriented model of role framing and attitude prediction 

    [[paper]](https://aclanthology.org/W17-6917.pdf)

    * `Object-oriented model`

    * ACL 2017 (2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics)

* Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models 

    [[paper]](https://aclanthology.org/D15-1018.pdf)

    * Lingjia Deng, Janyce Wiebe

    * EMNLP 2015

* FactBank: a corpus annotated with event factuality 

    [[paper]](https://www.researchgate.net/profile/Roser-Sauri/publication/220147734_FactBank_A_corpus_annotated_with_event_factuality/links/0f31753144a2cdc1b5000000/FactBank-A-corpus-annotated-with-event-factuality.pdf)

    * Roser Saurí, James Pustejovsky  

    * 2009

       

[Back to Top](#contents)

### Subsidiary Studies and Resources

* RIVETER Measuring Power and Social Dynamics Between Entities

  [[paper]](https://aclanthology.org/2023.acl-demo.36.pdf)

    * Maria Antoniak, Anjalie Field, Jimin Mun, Melanie Walsh, Lauren F. Klein, Maarten Sap

    * ACL-2023

* Multilingual Connotation Frames: A Case Study on Social Media

for Targeted Sentiment Analysis and Forecast 

    [[paper]](https://aclanthology.org/P17-2073.pdf)

    [[resources]](https://hrashkin.github.io/multicf.html)

    * Hannah Rashkin, Eric Bell, Yejin Choi, Svitlana Volkova

    * ACL-2017

* Learning Lexico-Functional Patterns for First-Person Affect 

    [[paper]](https://aclanthology.org/P17-2022.pdf)

    * Lena Reed, Jiaqi Wu, Shereen Oraby

    * ACL-2017 

* Understanding Abuse: A Typology of Abusive Language Detection Subtasks

    [[paper]](https://aclanthology.org/W17-3012.pdf)

    * Zeerak Waseem, Thomas Davidson, Dana Warmsley, Ingmar Weber

    * ACL-2017

* Connotation Frames: A Data-Driven Investigation

    [[paper]](https://aclanthology.org/P16-1030.pdf)

    * Hannah Rashkin, Sameer Singh, Yejin Choi 

    * ACL-2016

* Do Characters Abuse More Than Words?

    [[paper]](https://aclanthology.org/W16-3638.pdf)

    * Yashar Mehdad, Joel Tetreault

    * SIGDIAL-2016

   

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### Miscellaneous

* Verifying the robustness of opinion inference [[paper]](https://core.ac.uk/reader/83654780)

    * Josef Ruppenhofer, Jasper Brandes

    * KONVENS 2016

   

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## Thesises

* Mitigation of Gender Bias in Text using Unsupervised Controllable Rewriting [[master-thesis]](https://en.cs.uni-paderborn.de/fileadmin/informatik/fg/css/teaching/theses/brinkmann21-ma-thesis.pdf)

    * Maja Brinkmann

    * Paderborn University, 2022

    	* `Connotation Frames` (2.1.3.) 

    	* `Connotational Frames and Lexicon` (3.1.1.)

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## Datasets

* NOW (2010 -- present) 

  [[site]](https://www.corpusdata.org/now_corpus.asp) --

    News on the Web Corpus.

    * Contains data from online magazines and newspapers in 20 different English-speaking countries from 2010 to the current time.

    (Raw texts only).

* MPQA-3.0, (2015)

    [[site]](https://mpqa.cs.pitt.edu/) 

    [[paper]](https://aclanthology.org/N15-1146.pdf)

* SNLI 

    [[site]](https://nlp.stanford.edu/projects/snli/) 

    [[paper]](https://nlp.stanford.edu/pubs/snli_paper.pdf)  -- 

    Stanford Natural Language Inference 

    * 570k human-written English sentence pairs manually labeled for balanced classification with the labels 

    *entailment*, *contradiction*, and *neutral*

* FactBank 2009, 

    [[paper]](https://www.researchgate.net/profile/Roser-Sauri/publication/220147734_FactBank_A_corpus_annotated_with_event_factuality/links/0f31753144a2cdc1b5000000/FactBank-A-corpus-annotated-with-event-factuality.pdf) --

    a corpus annotated with event factuality

    * Consists of 208 documents and contains a total of 9,488, including TimeBank data;

manually annotated events.

* TimeBank, 2003

    [[site]](http://www.timeml.org/site/timebank/timebank.html)

    [[paper]](https://www.researchgate.net/profile/James-Pustejovsky/publication/228559081_The_TimeBank_corpus/links/09e4150ca6331b2eb9000000/The-TimeBank-corpus.pdf)

    * Annotated to indicate events, times, and temporal relations

   

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