Explainable Sentiment Analysis with Applications in Medicine
Abstract
Sentiment Analysis can help to extract knowledge related to opinions and emotions from user generated text information. It can be applied in medical field for patients monitoring purposes. With the availability of large datasets, deep learning algorithms have become a state of the art also for sentiment analysis. However, deep models have the drawback of not being non human-interpretable, raising various problems related to model's interpretability. Very few work have been proposed to build models that explain their decision making process and actions. In this work, we review the current sentiment analysis approaches and existing explainable systems. Moreover, we present a critical review of explainable sentiment analysis models and discussed the insight of applying explainable sentiment analysis in the medical field.
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Perturbation-based models that use model-agnostic explanations are common in the literature and have been used in two of the proposed CDSS.
“In terms of post-hoc explainability, perturbation-based models that use model-agnostic explanations are common in the literature [98,99,118,120,127] and have been used in two of the proposed CDSS [118,120].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [98]
Wang et al. identified that XAI could support different explanation types by articulating how people understand events or observations through explanations and can be leveraged to mitigate decision biases and cognitive biases.
“Wang et al. [118] identified that XAI could support different explanation types by articulating how people understand events or observations through explanations and can be leveraged to mitigate decision biases and cognitive biases [98,103,112].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [98]
Therefore, XAI can support compliance with the GDPR.
“Therefore, XAI can support compliance with the GDPR [98,103,106].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [98]
There is no universal definition of explainability nor a well-recognized equivalence or distinction between 'interpretable' and 'explainable' ML.
“There is no universal definition of explainability [48,98] nor a well-recognized equivalence or distinction between”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [98]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024 · see citing works
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,882 · see citing works
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167 · see citing works
Sentiment analysis algorithms and applications: A survey
Medhat, W., Hassan, A., & Korashy, H. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2014.04.011 · Cited by 3,265 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1150 · Cited by 3,081 · see citing works
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works
Deep learning for sentiment analysis: A survey
Zhang, L., Wang, S., & Liu, B. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1253 · Cited by 2,058 · see citing works
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works
Ekman, P. (1975) · Cited by 1,565 · see citing works
Young, P. T., & Arnold, M. B. (1963) · The American Journal of Psychology · doi:10.2307/1419805 · Cited by 1,437 · see citing works
Hinton, G. E., Krizhevsky, A., & Wang, S. D. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21735-7_6 · Cited by 1,360 · see citing works
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications
Ravi, K., & Ravi, V. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.015 · Cited by 1,319 · see citing works
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2017) · arXiv · doi:10.48550/arxiv.1711.11279 · Cited by 1,240 · see citing works
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181 · see citing works
Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164 · see citing works
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
dos Santos, C., & de Bayser, M. G. (2014) · International Conference on Computational Linguistics · Cited by 1,162 · see citing works
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., & Ma, S. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609579 · Cited by 814 · see citing works
Rationalizing Neural Predictions
Lei, T., Barzilay, R., & Jaakkola, T. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1011 · Cited by 777 · see citing works
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766 · see citing works
Model-Agnostic Interpretability of Machine Learning
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05386 · Cited by 699 · see citing works
Interpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687 · see citing works
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Generating Visual Explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534 · see citing works
Lesse, S. (1961) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1961.15.3.478 · Cited by 525 · see citing works
Current State of Text Sentiment Analysis from Opinion to Emotion Mining
Yadollahi, A., Shahraki, A. G., & Zai͏̈ane, O. R. (2017) · ACM Computing Surveys · doi:10.1145/3057270 · Cited by 489 · see citing works
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., Jha, S., Agrawal, M., Dziurzynski, L., Sap, M., et al. (2015) · Psychological Science · doi:10.1177/0956797614557867 · Cited by 487 · see citing works
Detecting suicidality on Twitter
O’Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C. L., & Christensen, H. M. (2015) · Internet Interventions · doi:10.1016/j.invent.2015.03.005 · Cited by 439 · see citing works
Sentiment Analysis Is a Big Suitcase
Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017) · IEEE Intelligent Systems · doi:10.1109/mis.2017.4531228 · Cited by 439 · see citing works
EMOTION: Theory, Research, and Experience
(1980) · Elsevier eBooks · doi:10.1016/b978-0-12-558701-3.50001-6 · Cited by 422 · see citing works
Investigating Capsule Networks with Dynamic Routing for Text Classification
Yang, M., Zhao, W., Ye, J., Lei, Z., Zhao, Z., & Zhang, S. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1350 · Cited by 408 · see citing works
Sentiment classification: The contribution of ensemble learning
Wang, G., Sun, J., Ma, J., Xu, K. S. J., & Gu, J. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.08.002 · Cited by 378 · see citing works
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Arras, L., Montavon, G., Müller, K.-R., & Samek, W. (2017) · Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis · doi:10.18653/v1/w17-5221 · Cited by 361 · see citing works
Improving Robot Controller Transparency Through Autonomous Policy Explanation
Hayes, B., & Shah, J. A. (2017) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/2909824.3020233 · Cited by 359 · see citing works
Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products
Myslín, M., Zhu, S., Chapman, W. W., & Conway, M. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2534 · Cited by 323 · see citing works
Measuring post traumatic stress disorder in Twitter
Coppersmith, G., Harman, C., & Dredze, M. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14574 · Cited by 316 · see citing works
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Ras, G., van Gerven, M., & Haselager, P. (2018) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-319-98131-4_2 · Cited by 255 · see citing works
A hybrid approach to the sentiment analysis problem at the sentence level
Appel, O., Chiclana, F., Carter, J., & Fujita, H. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.040 · Cited by 225 · see citing works
Sentiment Analysis by Capsules
Wang, Y., Sun, A., Han, J., Liu, Y., & Zhu, X. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186015 · Cited by 214 · see citing works
Refining Word Embeddings for Sentiment Analysis
Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1056 · Cited by 195 · see citing works
DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
Suhara, Y., Xu, Y., & Pentland, A. '. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052676 · Cited by 163 · see citing works
EmoTxt: A toolkit for emotion recognition from text
Calefato, F., Lanubile, F., & Novielli, N. (2017) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2017.8272591 · Cited by 123 · see citing works
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter ValuesPreprint
Adebayo, J., Gilmer, J., Goodfellow, I., & Kim, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03307 · Cited by 101 · see citing works
Explaining Predictions of Non-Linear Classifiers in NLP
Arras, L., Horn, F., Montavon, G., Müller, K., & Samek, W. (2016) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w16-1601 · Cited by 100 · see citing works
Sentiment analysis and affective computing for depression monitoring
Zucco, C., Calabrese, B., & Cannataro, M. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217966 · Cited by 96 · see citing works
Self-citationEMOTION DETECTION IN EMAIL CUSTOMER CARE
Gupta, N., Gilbert, M., & Fabbrizio, G. D. (2012) · Computational Intelligence · doi:10.1111/j.1467-8640.2012.00454.x · Cited by 82 · see citing works
Improving action selection in MDP's via knowledge transfer
Sherstov, A. A., & Stone, P. (2005) · National Conference on Artificial Intelligence · Cited by 79 · see citing works
Rule Extraction Algorithm for Deep Neural Networks: A ReviewPreprint
Hailesilassie, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05267 · Cited by 76 · see citing works
IMPROVED EXPLAINABILITY OF CAPSULE NETWORKS: RELEVANCE PATH BY AGREEMENT
Shahroudnejad, A., Afshar, P., Plataniotis, K. N., & Mohammadi, A. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646474 · Cited by 71 · see citing works
Mining Twitter Data for Potential Drug Effects
Jiang, K., & Zheng, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53914-5_37 · Cited by 68 · see citing works
Cole-Lewis, H. J., Varghese, A., Sanders, A. E., Schwarz, M., Pugatch, J., & Augustson, E. M. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4392 · Cited by 68 · see citing works
Contrastive Explanations for Reinforcement Learning in terms of Expected ConsequencesPreprint
van der Waa, J., van Diggelen, J., den Bosch, K. V., & Neerincx, M. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.08706 · Cited by 66 · see citing works
Contextual Explanation NetworksPreprint
Al-Shedivat, M., Dubey, A., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10301 · Cited by 64 · see citing works
Deep Belief Networks with Feature Selection for Sentiment Classification
Ruangkanokmas, P., Achalakul, T., & Akkarajitsakul, K. (2016) · International Conference on Intelligent Systems, Modelling and Simulation · doi:10.1109/isms.2016.9 · Cited by 55 · see citing works
Bayesian Rule Sets for Interpretable Classification
Wang, T., Rudin, C. D., Doshi‐Velez, F., Liu, Y., Klampfl, E., & MacNeille, P. (2016) · Proceedings · doi:10.1109/icdm.2016.0171 · Cited by 46 · see citing works
Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
Mowery, D. L., Park, A., Bryan, C. J., & Conway, M. (2016) · International Conference on Computational Linguistics · Cited by 46 · see citing works
Wang, T., Rudin, C., Doshi-Velez, F., Liu, Y.-M., Klampfl, E., & MacNeille, P. (2015) · arXiv.org · doi:10.48550/arxiv.1504.07614 · Cited by 41 · see citing works
Stacked denoising autoencoders for sentiment analysis: a review
Sagha, H., Cummins, N., & Schuller, B. W. (2017) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1212 · Cited by 38 · see citing works
Using Twitter Data and Sentiment Analysis to Study Diseases Dynamics
Carchiolo, V., Longheu, A., & Malgeri, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22741-2_2 · Cited by 35 · see citing works
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
Panchenko, A., Ruppert, E., Faralli, S., Ponzetto, S. P., & Biemann, C. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1009 · Cited by 29 · see citing works
Affective social network—happiness inducing social media platform
Kim, H., Park, S.-B., & Jo, G. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1157-2 · Cited by 19 · see citing works
Using Social Networks Data for Behavior and Sentiment Analysis
Calabrese, B., Cannataro, M., & Ielpo, N. (2015) · International Conference on Internet and Distributed Computing Systems · doi:10.1007/978-3-319-23237-9_25 · Cited by 10 · see citing works
Self-citationSundararajan, M., & Taly, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04205 · Cited by 10 · see citing works
Computational challenges for sentiment analysis in life sciences
Ciullo, F., Zucco, C., Calabrese, B., Agapito, G., Guzzi, P. H., & Cannataro, M. (2016) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2016.7568365 · Cited by 9 · see citing works
Self-citationDeep belief networks for sentiment analysis
Jin, Y. (2017) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 75 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Bharati, S., Mondal, M. R. H., & Podder, P. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3266418 · Cited by 235 · see citing works
Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data
Gaur, L., Bhandari, M., Razdan, T., Mallik, S., & Zhao, Z. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.822666 · Cited by 173 · see citing works
An emotion and cognitive based analysis of mental health disorders from social media data
Uban, A. S., Chulvi, B., & Rosso, P. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.05.032 · Cited by 146 · see citing works
XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process
Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010006 · Cited by 131 · see citing works
A Survey on Trustworthy Recommender Systems
Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., Li, Y., Xian, Y., & Zhang, Y. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3652891 · Cited by 144 · see citing works
Mertes, S., Huber, T., Weitz, K., Heimerl, A., & André, E. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.825565 · Cited by 119 · see citing works
Kusal, S., Patil, S., Choudrie, J., Kotecha, K. V., Vora, D. R., & Pappas, I. O. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10509-0 · Cited by 159 · see citing works
A review on emotion detection by using deep learning techniques
Chutia, T., & Baruah, N. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10831-1 · Cited by 95 · see citing works
Sentiment analysis of clinical narratives: A scoping review
Denecke, K., & Reichenpfader, D. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104336 · Cited by 83 · see citing works
de Souza, L. A., Jr., Mendel, R., Sophia, S., Ebigbo, A., Probst, A., Messmann, H., Papa, J. P., & Palm, C. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104578 · Cited by 73 · see citing works
An explanation framework and method for AI-based text emotion analysis and visualisation
Li, Y., Chan, J., Peko, G. M., & Sundaram, D. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114121 · Cited by 58 · see citing works
A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution
Liang, H., Ganeshbabu, U., & Thorne, T. (2020) · IEEE Access · doi:10.1109/access.2020.2979012 · Cited by 49 · see citing works
Self-citationIddrisu, A.-M., Mensah, S., Boafo, F., Yeluripati, G. R., & Kudjo, P. K. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100180 · Cited by 31 · see citing works
Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail
Cirqueira, D., do Carmo, F. A., Cakir, G., Jacob, A. F. L., Lobato, F. M. F., Bezbradica, M., & Helfert, M. (2020) · International Conference on Computer-Human Interaction Research and Applications (CHIRA) · doi:10.5220/0010215303190328 · Cited by 33 · see citing works
Multi-view informed attention-based model for Irony and Satire detection in Spanish variants
Bueno, R. O., Rosso, P., & Pagóla, J. E. M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107597 · Cited by 27 · see citing works
Pilar, G. D., Berriel, I. S., Diego, P.-M., & Luis, G.-Á. J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118817 · Cited by 23 · see citing works
Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types
Sanglerdsinlapachai, N., Plangprasopchok, A., Ho, T. B., & Nantajeewarawat, E. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102033 · Cited by 20 · see citing works
Gambo, I., Massenon, R., Lin, C., Ogundokun, R. O., Agarwal, S., & Pak, W. (2024) · IEEE Access · doi:10.1109/access.2024.3443527 · Cited by 19 · see citing works
Understanding Patterns of Anorexia Manifestations in Social Media Data with Deep Learning
Uban, A. S., Chulvi, B., & Rosso, P. (2021) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/2021.clpsych-1.24 · Cited by 19 · see citing works
Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
Li, X., Sun, X., Xu, Z., & Zhou, Y. (2021) · International Conference on Imaging, Signal Processing and Communications (ICISPC) · doi:10.1109/icispc53419.2021.00024 · Cited by 18 · see citing works
An Overview of Explainable AI Methods, Forms and Frameworks
Kumar, D., & Mehta, M. A. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-12807-3_3 · Cited by 19 · see citing works
Interpretable Bangla Sarcasm Detection using BERT and Explainable AI
Anan, R., Apon, T. S., Hossain, Z. T., Modhu, E. A., Mondal, S., & Alam, M. G. R. (2023) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc57344.2023.10099331 · Cited by 20 · see citing works
Recommender Systems Meet Large Language Model Agents: A Survey
Zhu, X., Wang, Y., Gao, H., Xu, W., Wang, C., Liu, Z., Wang, K., Jin, M., Pang, L. X., Weng, Q., et al. (2025) · Foundations and Trends® in Privacy and Security · doi:10.1561/3300000050 · Cited by 21 · see citing works
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Shawi, R. E., & Al-Mallah, M. H. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14019 · Cited by 15 · see citing works
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Uban, A. S., Chulvi, B., & Rosso, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80599-9_27 · Cited by 15 · see citing works
Yang, G., Ye, Q., & Xia, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01998 · Cited by 13 · see citing works
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Oliveira, M., Wilming, R., Clark, B., Budding, C., Eitel, F., Ritter, K., & Haufe, S. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1330919 · Cited by 12 · see citing works
Milano, M., Zucco, C., & Cannataro, M. (2021) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-021-00323-5 · Cited by 11 · see citing works
Self-citationGagliardi, G., Alfeo, A. L., Catrambone, V., Cimino, M. G. C. A., Vos, M. D., & Valenza, G. (2023) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner52421.2023.10123758 · Cited by 10 · see citing works
Uban, A. S., Chulvi, B., & Rosso, P. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-04431-1_13 · Cited by 9 · see citing works
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Kanojia, D., & Joshi, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-390535-0.00008-2 · Cited by 9 · see citing works
Artificial intelligence models in digital twins for health and well-being
Ferdousi, R., Laamarti, F., & Saddik, A. E. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-399163-6.00011-1 · Cited by 9 · see citing works
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Silveira, T., Uszkoreit, H., & Ai, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32236-6_56 · Cited by 10 · see citing works
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Jang, H., & Yoon, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110984 · Cited by 7 · see citing works
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Muhammad, S. H., Brazdil, P., & Jorge, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45442-5_81 · Cited by 6 · see citing works
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Tafannum, F., Shopnil, M. N. S., Salsabil, A., Ahmed, N., Alam, M. G. R., & Reza, M. T. (2021) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon53757.2021.9666567 · Cited by 6 · see citing works
Explainable Artificial Intelligence (XAI) in Healthcare
Manek, A. S., Vashist, S., Tripathi, G., & Sindhu, S. (2025) · doi:10.1002/9781394249312.ch2 · Cited by 6 · see citing works
Machine Learning-based Depression Prediction using Social Media Feeds
Keerthiga, M., Abisha, D., Kalaiselvi, P., & Shenbagalakshmi, S. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134427 · Cited by 5 · see citing works
Layer-selective deep representation to improve esophageal cancer classification
de Souza, L. A., Jr., Júnior, L. A. P., Santana, M. C. S., Mendel, R., Rauber, D., Ebigbo, A., Probst, A., Messmann, H., Papa, J. P., & Palm, C. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03142-8 · Cited by 3 · see citing works
BERT Model with Fuzzy Logic Optimization on Multivariate Sentiment Analysis Tasks
Sun, C. (2024) · Transactions on Computer Science and Intelligent Systems Research · doi:10.62051/48xrya81 · Cited by 3 · see citing works
Beyond just saying it’s false: explainable AI for multimodal misinformation detection
Roy, S., Bhanu, M., Priya, S., Chandra, J., & Dandapat, S. K. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06656-2 · Cited by 2 · see citing works
Mertes, S., Huber, T., Weitz, K., Heimerl, A., & André, E. (2020) · Cited by 2 · see citing works
Explainable Sentiment Analysis
Denecke, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-30187-2_17 · Cited by 1 · see citing works
The Future of Medical Sentiment Analysis
Denecke, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-30187-2_18 · Cited by 1 · see citing works
Explainable artificial intelligence for gastrointestinal cancer using CNN-a review
Renjith, V. R., & Judith, J. E. (2023) · AIP conference proceedings · doi:10.1063/5.0172064 · Cited by 1 · see citing works
Makuwe, B., Mabokela, K. R., & Schlippe, T. (2023) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii59096.2023.10388095 · Cited by 1 · see citing works
Challenges and Ethical Considerations of Emotion Detection
Cavicchio, F. (2024) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-72047-5_7 · Cited by 1 · see citing works
Cheng, C., Cao, S., Tang, G., Ma, F., Cui, D., & Madhumitha, S. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0461 · Cited by 1 · see citing works
From medical imaging to explainable artificial intelligence
Tjoa, E. (2022) · doi:10.32657/10356/164493 · Cited by 1 · see citing works
AI with Consciousness: Advancing Explainability in the Healthcare Sector
Ticku, A., Gupta, S. K., & Gupta, B. (2023) · International Conference on Communication, Security and Artificial Intelligence (ICCSAI) · doi:10.1109/iccsai59793.2023.10421388
Scarpino, I., & Vallelunga, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00211-6
Anuprathibha, T., Pravin Kumar, M., Sakthi, G., & Rajkumar, K. K. (2025) · Journal of Machine and Computing · doi:10.53759/7669/jmc202505093
An Unified Framework for Interpretable Sentiment Analysis Using Explainable AI
Dhanalakshmi, R. V., Shashank, M. P., Sushmita, K., Nithin, K. J., Keerthana, S. N., & Gour, S. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-10047-4_20
- Authors
- Zucco, C., Liang, H., Fatta, G. D., & Cannataro, M.
- Affiliations
- Magna Graecia University (IT)
- University of Reading (GB)
- Venue
- 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 1740-1747
- OpenAlex
- W2912771644
- Cited by
- 59
- References
- 75
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 9 · 2023: 16 · 2022: 7 · 2021: 13 · 2020: 5 · 2019: 1
- FWCI
- 1.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publishedVersion)
- Open Research (University of Surrey) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- sentiment analysis, model interpretability, explainable AI, medical applications, patient monitoring, user-generated text, deep learning models, clinical text mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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