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Reading list for Awesome Sentiment Analysis papers
| Date | Stars |
|---|---|
| 2026-07-24 | 537 |
| 2026-07-25 | 537 |
| 2026-07-28 | 537 |
| 2026-07-30 | 537 |
| 2026-08-06 | 537 |
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# 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. IEEE Transactions on Affective Computing (2020). ## Citation If you find this repository useful in your research, you may cite the paper below where we briefly cover the progress in sentiment analysis and further address the key challenges and new directions in this domain. >> [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. IEEE Transactions on Affective Computing (2020). ## New Directions in Sentiment Analysis  ## 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) - [Robust Sentiment Analysis](#robust-sentiment-analysis) ## 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) ## Aspec
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Read on GitHub
Soujanya Poria · Nanyang Technological University · Singapore
35
Navonil Majumder
5
Feiyang(Vance) Chen · Creatify AI · United States
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c0dc71fbda1632fe, topic:sentiment-analysis, name:sentiment analysis, desc:sentiment analysis