# awesome-sentiment-analysis **Repository Path**: www.ydj.com/awesome-sentiment-analysis ## Basic Information - **Project Name**: awesome-sentiment-analysis - **Description**: 情感分析论文的阅读清单 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 1 - **Created**: 2020-06-20 - **Last Updated**: 2022-05-26 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Reading list for Awesome Sentiment Analysis papers Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks — such as sentiment polarity classification — and datasets, there is an underlying perception that this field has reached its maturity. > Interested to know our take on the current challenges and future directions of this field using the following papers as compass? > >> Read this paper - [Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research.](https://arxiv.org/pdf/2005.00357.pdf) Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, and Rada Mihalcea. 2020. arXiv preprint arXiv:2005.00357 ## New Directions in Sentiment Analysis ![Alt text](org2-new.png?raw=true "Title") ## Beginner's Guide (Must-Read Papers) - [Effects of adjective orientation and gradability on sentence subjectivity](https://www.aclweb.org/anthology/C00-1044.pdf) - [Word sense and subjectivity](http://people.cs.pitt.edu/~wiebe/pubs/papers/acl06.pdf) - [Thumbs up?: sentiment classification using machine learning techniques](https://www.aclweb.org/anthology/W02-1011.pdf) - [Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews]() - [A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts](https://www.aclweb.org/anthology/P04-1035.pdf) - [Mining and summarizing customer reviews](https://dl.acm.org/doi/10.1145/1014052.1014073) - [Recursive deep models for semantic compositionality over a sentiment treebank](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf) - [Convolutional neural networks for sentence classification](https://www.aclweb.org/anthology/D14-1181.pdf) - [Contextual valence shifters](https://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-020.pdf) - [SENTIWORDNET: A publicly available lexical resource for opinion mining](http://www.esuli.it/publications/LREC2006.pdf) ## Topics - [Aspect-based Sentiment Analysis](#aspect-based-sentiment-analysis) - [Multimodal Sentiment Analysis](#multimodal-sentiment-analysis) - [Contextual Sentiment Analysis](#contextual-sentiment-analysis) - [Sentiment Reasoning](#sentiment-reasoning) - [Sarcasm Analysis](#sarcasm-analysis) - [Domain Adaptation](#domain-adaptation) - [Multilingual Sentiment Analysis](#multilingual-sentiment-analysis) - [Sentiment-aware NLG](#sentiment-aware-nlg) - [Bias in Sentiment Analysis Systems](#bias-in-sentiment-analysis-systems) ## Survey, Books, and Opinion Pieces - [Sentiment Analysis and Opinion Mining](https://www.morganclaypool.com/doi/abs/10.2200/s00416ed1v01y201204hlt016) - [Challenges in Sentiment Analysis](https://saifmohammad.com/WebDocs/sentiment-challenges.pdf) - [Automatic Sarcasm Detection: A Survey](https://dl.acm.org/doi/10.1145/3124420) - [Generating natural language under pragmatic constraints](https://dl.acm.org/doi/book/10.5555/535378) - [A survey of opinion mining and sentiment analysis](https://link.springer.com/chapter/10.1007/978-1-4614-3223-4_13) - [A survey on opinion mining and sentiment analysis: Tasks, approaches and applications](https://www.sciencedirect.com/science/article/pii/S0950705115002336) ## Aspect-based Sentiment Analysis - [Mining and summarizing customer reviews](https://dl.acm.org/doi/10.1145/1014052.1014073) - [Topic sentiment mixture: modeling facets and opinions in weblogs](https://dl.acm.org/doi/10.1145/1242572.1242596) - [Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification](https://www.aclweb.org/anthology/P11-1013/) - [Opinion Word Expansion and Target Extraction through Double Propagation](https://www.mitpressjournals.org/doi/pdf/10.1162/coli_a_00034) - [Automated Rule Selection for Aspect Extraction in Opinion Mining](https://www.ijcai.org/Proceedings/15/Papers/186.pdf) - [Aspect extraction for opinion mining with a deep convolutional neural network](https://www.sentic.net/aspect-extraction-for-opinion-mining.pdf) - [Lifelong Learning CRF for Supervised Aspect Extraction](https://doi.org/10.18653/v1/P17-2023) - [Attention-based LSTM for aspect-level sentiment classification](https://www.aclweb.org/anthology/D16-1058/) - [Dyadic Memory Networks for Aspect-based Sentiment Analysis](https://doi.org/10.1145/3132847.3132936) - [Aspect Specific Sentiment Analysis using Hierarchical Deep Learning](http://nyc.lti.cs.cmu.edu/classes/11-741/Papers/lakkaraju-nips-wksp14.pdf) - [DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction](https://www.aclweb.org/anthology/P19-1056/) ## Multimodal Sentiment Analysis - [Multimodal sentiment analysis](https://dl.acm.org/doi/10.5555/2392963.2392965) - [Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis](https://www.aclweb.org/anthology/D15-1303/) - [Context-Dependent Sentiment Analysis in User-Generated Videos](https://doi.org/10.18653/v1/P17-1081) - [Multimodal sentiment analysis using hierarchical fusion with context modeling](https://doi.org/10.1016/j.knosys.2018.07.041) - [Tensor Fusion Network for Multimodal Sentiment Analysis](https://www.aclweb.org/anthology/D17-1115/) - [Efficient Low-rank Multimodal Fusion With Modality-Specific Factors](https://www.aclweb.org/anthology/P18-1209/) - [Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph](https://www.aclweb.org/anthology/P18-1208/) - [Memory Fusion Network for Multi-view Sequential Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17341/16122) - [Multi-attention Recurrent Network for Human Communication Comprehension](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17390) - [Contextual Inter-modal Attention for Multi-modal Sentiment Analysis](https://doi.org/10.18653/v1/d18-1382) - [Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis](https://doi.org/10.18653/v1/D19-1566) - [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://www.aclweb.org/anthology/P19-1656/) - [Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis](https://www.aclweb.org/anthology/W18-3308.pdf) - [Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4666) - [Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion](http://arxiv.org/abs/1911.07848) ## Contextual Sentiment Analysis - [Coarse-grained +/-effect word sense disambiguation for implicit sentiment analysis](https://web.eecs.umich.edu/~mihalcea/papers/choi.ieeetac17.pdf) - [Sentiment propagation via implicature constraints](https://www.aclweb.org/anthology/E14-1040.pdf) - [Context-dependent sentiment analysis in user-generated videos](https://www.aclweb.org/anthology/P17-1081.pdf) - [Enhanced twitter sentiment classification using contextual information](https://www.aclweb.org/anthology/W15-2904.pdf) - [Contextual valence shifters](https://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-07/SS04-07-020.pdf) ## Sentiment Reasoning ### Who - [End-to-end joint opinion role labeling with BERT](https://ieeexplore.ieee.org/document/9006119) - [Joint inference for fine-grained opinion extraction](https://www.aclweb.org/anthology/P13-1161.pdf) ### Why - [Reflections on Sentiment/Opinion Analysis](https://www.cs.cmu.edu/~hovy/papers/15sentiment-li-hovy.pdf) - [Coarse-grained +/-effect word sense disambiguation for implicit sentiment analysis](https://web.eecs.umich.edu/~mihalcea/papers/choi.ieeetac17.pdf) - [Sentiment propagation via implicature constraints](https://www.aclweb.org/anthology/E14-1040.pdf) ## Sarcasm Analysis - [ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews](https://www.cs.huji.ac.il/~arir/10-sarcasmAmazonICWSM10.pdf) - [Identifying Sarcasm in Twitter: A Closer Look](https://www.aclweb.org/anthology/P11-2102.pdf) - [Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis](http://www.lrec-conf.org/proceedings/lrec2014/pdf/67_Paper.pdf) - [Sarcasm as Contrast between a Positive Sentiment and Negative Situation](https://www.aclweb.org/anthology/D13-1066.pdf) - [Your Sentiment Precedes You: Using an author’s historical tweets to predict sarcasm](https://www.aclweb.org/anthology/W15-2905.pdf) - [Contextualized Sarcasm Detection on Twitter](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/download/10538/10445) - [Exploring Author Context for Detecting Intended vs Perceived Sarcasm](https://www.aclweb.org/anthology/P19-1275.pdf) - [Harnessing Context Incongruity for Sarcasm Detection](https://www.aclweb.org/anthology/P15-2124.pdf) - [Sarcasm Analysis Using Conversation Context](https://www.aclweb.org/anthology/J18-4009.pdf) - [Reasoning with Sarcasm by Reading In-between](https://www.aclweb.org/anthology/P18-1093.pdf) - [Detecting Sarcasm in Multimodal Social Platforms](https://dl.acm.org/doi/10.1145/2964284.2964321) - [Harnessing Cognitive Features for Sarcasm Detection](https://www.aclweb.org/anthology/P16-1104.pdf) - [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://www.aclweb.org/anthology/C18-1156.pdf) - [The Effect of Sociocultural Variables on Sarcasm Communication Online](https://arxiv.org/pdf/2004.04945.pdf) - [iSarcasm: A Dataset of Intended Sarcasm](https://arxiv.org/pdf/1911.03123.pdf) - [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://www.aclweb.org/anthology/P19-1455.pdf) ## Domain Adaptation - [Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification](https://www.aclweb.org/anthology/P07-1056.pdf) - [Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach](http://www.icml-2011.org/papers/342_icmlpaper.pdf) - [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://www.aclweb.org/anthology/D16-1057/) - [Marginalized Denoising Autoencoders for Domain Adaptation](https://dl.acm.org/doi/10.5555/3042573.3042781) - [Unsupervised Domain Adaptation by Backpropagation](http://proceedings.mlr.press/v37/ganin15.pdf) - [Domain Separation Networks](http://papers.nips.cc/paper/6254-domain-separation-networks) - [Cross-domain sentiment classification via spectral feature alignment](https://dl.acm.org/doi/10.1145/1772690.1772767) - [Pivot Based Language Modeling for Improved Neural Domain Adaptation](https://www.aclweb.org/anthology/N18-1112/) ## Multilingual Sentiment Analysis - [Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques](https://doi.org/10.1007/s12559-016-9421-9) - [Comparing the Level of Code-Switching in Corpora](http://www.lrec-conf.org/proceedings/lrec2016/pdf/669_Paper.pdf) - [De-Mixing Sentiment from Code-Mixed Text](https://www.aclweb.org/anthology/P19-2052.pdf) ## Sentiment-aware NLG - [The effects of affective interventions in human-computer interaction](https://academic.oup.com/iwc/article/16/2/295/723724) - [Predicting and Eliciting Addressee’s Emotion in Online Dialogue](https://www.aclweb.org/anthology/P13-1095.pdf) - [Emotional chatting machine: Emotional conversation generation with internal and external memory](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16455/15753) - [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://www.aclweb.org/anthology/P17-1059/) - [A Pattern-Based Model for Generating Text to Express Emotion](https://link.springer.com/chapter/10.1007/978-3-642-24571-8_2) - [Toward Controlled Generation of Text](http://proceedings.mlr.press/v70/hu17e.html) - [SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks](https://www.ijcai.org/Proceedings/2018/0618.pdf) - [Learning to Generate Reviews and Discovering Sentiment](https://arxiv.org/abs/1704.01444) - [Learning to Generate Product Reviews from Attributes](https://www.aclweb.org/anthology/E17-1059/) - [Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer](https://www.aclweb.org/anthology/N18-1169/) - [Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://www.aclweb.org/anthology/P19-1041/) - [Style Transfer from Non-Parallel Text by Cross-Alignment](https://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment.pdf) ## Bias in Sentiment Analysis Systems - [Examining gender and race bias in two hundred sentiment analysis systems](https://www.aclweb.org/anthology/S18-2005.pdf) - [Exploring demographic language variations to improve multilingual sentiment analysis in social media](https://www.aclweb.org/anthology/D13-1187.pdf) - [The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064417)