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Chen, Qian, Ragusa, Edoardo, Chaturvedi, Iti, Cambria, Erik, and Zunino, Rodolfo (2023) Text-Image Sentiment Analysis. In: Lecture Notes in Computer Science (13397) pp. 169-180. From: CICLing 2018: 19th International Conference on Computational Linguistics and Intelligent Text Processing, 18–24 March 2018, Hanoi, Vietnam.

Chaturvedi, Iti, Cambria, Erik, Cavallari, Sandro, and Welsch, Roy E. (2020) Genetic programming for domain adaptation in product reviews. In: Proceedings of the IEEE Congress on Evolutionary Computation. From: CEC 2020: IEEE Congress on Evolutionary Computation, 19-24 July 2020, Glasgow, UK.

Chaturvedi, Iti, Satapathy, Ranjan, Cavallari, Sandro, and Cambria, Erik (2019) Fuzzy commonsense reasoning for multimodal sentiment analysis. Pattern Recognition Letters, 125. pp. 264-270.

Chaturvedi, Iti, Ragusa, Edoardo, Gastaldo, Paolo, Zunino, Rodolfo, and Cambria, Erik (2018) Bayesian network based extreme learning machine for subjectivity detection. Journal of The Franklin Institute, 355 (4). pp. 1780-1797.

Chaturvedi, Iti, Cambria, Erik, Welsch, Roy, and Herrera, Francisco (2018) Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Information Fusion, 44. pp. 65-77.

Chaturvedi, Iti, Cambria, Erik, Poria, Soujanya, and Bajpai, Rajiv (2016) Bayesian deep convolution belief networks for subjectivity detection. In: Proceedings of the IEEE International Conference on Data Mining Workshops. 7836765. pp. 916-923. From: ICDMW 2016: 16th International Conference on Data Mining Workshops, 12-15 December 2015, Barcelona, Spain.

Chaturvedi, Iti, Ong, Yew-Soon, Tsang, Ivor W., Welsch, Roy E., and Cambria, Erik (2016) Learning word dependencies in text by means of a deep recurrent belief network. Knowledge Based Systems, 108. pp. 144-154.

Chaturvedi, Iti, Cambria, Erik, and Vilares, David (2016) Lyapunov filtering of objectivity for Spanish sentiment model. In: Proceedings of the International Joint Conference on Neural Networks. pp. 4474-4481. From: IJCNN: 2016 International Joint Conference on Neural Networks, 24-29 July 2016, Vancouver, Canada.

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Hussain, Sadam, Lafarga-Osuna, Yareth, Ali, Mansoor, Naseem, Usman, Ahmed, Masroor, and Tamez-Peña, Jose Gerardo (2023) Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review. BMC Bioinformatics, 24 (1). 401.

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Ji, Junhui, Ren, Wei, and Naseem, Usman (2023) Identifying Creative Harmful Memes via Prompt based Approach. In: Proceedings of the ACM Web Conference 2023. pp. 3868-3872. From: WWW '23: The ACM Web Conference 2023, April 30 - May 4 2023, Austin, TX, USA.

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Khatua, Aparup, Cambria, Erik, Khatua, Apalak, and Chaturvedi, Iti (2017) Let's chat about Brexit! A politically-sensitive dialog system based on Twitter data. In: Proceedings of the IEEE International Conference on Data Mining Workshops. pp. 393-398. From: ICDMW 2017: 17th IEEE International Conference on Data Mining Workshops, 18-21 November 2017, New Orleans, LA, USA.

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Liu, Sisi, and Lee, Ickjai (2021) Sequence encoding incorporated CNN model for Email document sentiment classification. Applied Soft Computing, 102. 107104.

Liu, Sisi, and Lee, Ickjai (2019) Extracting features with medical sentiment lexicon and position encoding for drug reviews. Health Information Science and Systems, 7 (11).

Lei, Lei, Qi, Jiaju, and Zheng, Kan (2019) Patent analytics based on feature Vector Space Model: a case of IoT. IEEE Access, 7. pp. 45705-45715.

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Mustafa, Akram, and Rahimi Azghadi, Mostafa (2021) Automated machine learning for healthcare and clinical notes analysis. Computers, 10 (2). 24.

Mondal, Anupam, Chaturvedi, Iti, Das, Dipankar, Bajpai, Rajiv, and Bandyopadhyay, Sivaji (2016) Lexical resource for medical events: a polarity based approach. In: Proceedings of the IEEE International Conference on Data Mining Workshops. pp. 1302-1309. From: ICDMW 2015: 15th IEEE International Conference on Data Mining Workshops, 14-17 November 2015, Atlantic City, NY, USA.

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Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2023) Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media. In: Proceedings of the ACM Web Conference 2023. pp. 995-1003. From: WWW 2023: The ACM Web Conference 2023: Companion of The World Wide Web Conference, April 30 - May 4 2023, Austin, TX, USA.

Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2023) Hybrid Text Representation for Explainable Suicide Risk Identification on Social Media. IEEE Transactions on Computational Social Systems. (In Press)

Naseem, Usman, Khushi, Matloob, Dunn, Adam G., and Kim, Jinman (2023) K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering. IEEE Journal of Biomedical and Health Informatics. (In Press)

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2023) A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining. pp. 706-714. From: WSDM '23: 16th ACM International Conference on Web Search and Data Mining, 27 February - 3 March 2023, Singapore.

Naseem, Usman, Lee, Byoung Chan, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2022) Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model. In: Proceedings of First Workshop on Efficient Benchmarking in NLP. pp. 22-31. From: NLP Power 2022: First Workshop on Efficient Benchmarking in NLP, 26 May 2022, Dublin, IRL.

Naseem, Usman, Dunn, Adam G., Khushi, Matloob, and Kim, Jinman (2022) Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT. BMC Bioinformatics, 23. 144.

Naseem, Usman, Dunn, Adam G., Kim, Jinman, and Khushi, Matloob (2022) Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification. In: Proceedings of the ACM Web Conference 2022. pp. 2563-2572. From: WWW '22: the ACM Web Conference 2022, 25-29 April 2022, Lyon, France.

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2022) Identification of Disease or Symptom terms in Reddit to Improve Health Mention Classification. In: Proceedings of the ACM Web Conference 2022. pp. 2573-2581. From: WWW '22: the ACM Web Conference 2022, 25-29 April 2022, Lyon, France.

Naseem, Usman, Bandi, Ajay, Raza, Shaina, Rashid, Junaid, and Chakravarthi, Bharathi Raja (2022) Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation. In: Proceedings of the Proceedings of the 21st Workshop on Biomedical Language Processing. pp. 110-115. From: BioNLP 22: 21st Workshop on Biomedical Language Processing, 26 May 2022, Dublin, IRL.

Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2022) RHMD: A Real-World Dataset for Health Mention Classification on Reddit. IEEE Transactions on Computational Social Systems. (In Press)

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2022) Robust Identification of Figurative Language in Personal Health Mentions on Twitter. IEEE Transactions on Artificial Intelligence, 4 (2). pp. 362-372.

Naseem, Usman, Khushi, Matloob, and Kim, Jinman (2022) Vision-Language Transformer for Interpretable Pathology Visual Question Answering. IEEE Journal of Biomedical and Health Informatics, 27 (4). pp. 1681-1690.

Naseem, Usman, Khushi, Matloob, Reddy, Vinay, Rajendran, Sakthivel, Razzak, Imran, and Kim, Jinman (2021) BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition. In: Proceedings of the 2021 International Joint Conference on Neural Networks. From: IJCNN: 2021 International Joint Conference on Neural Networks, 18-22 July 2021, Shenzhen, China.

Naseem, Usman, Razzak, Imran, Khushi, Matloob, Eklund, Peter W., and Kim, Jinman (2021) COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis. IEEE Transactions on Computational Social Systems, 8 (4). pp. 1003-1015.

Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam (2021) Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU. In: Proceedings of the 2021 International Joint Conference on Neural Networks. From: IJCNN: 2021 International Joint Conference on Neural Networks, 18-22 July 2021, Shenzhen, China.

Naseem, Usman, Razzak, Imran, Khan, Shah Khalid, and Prasad, Mukesh (2021) A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models. ACM Transactions on Asian and Low-Resource Language Information Processing, 20 (5). 74.

Naseem, Usman, Musial, Katarzyna, Eklund, Peter, and Prasad, Mukesh (2020) Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding. In: Proceedings of the 2020 International Joint Conference on Neural Networks. From: IJCNN: 2020 International Joint Conference on Neural Networks, 19-24 July 2020, Glasgow, UK.

Naseem, Usman, Razzak, Imran, Eklund, Peter, and Musial, Katarzyna (2020) Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm. In: Proceedings of the 2020 International Joint Conference on Neural Networks. From: IJCNN: 2020 International Joint Conference on Neural Networks, 19-24 July 2020, Glasgow, UK.

Naseem, Usman, Razzak, Imran, Musial, Katarzyna, and Imran, Muhammad (2020) Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis. Future Generation Computer Systems, 113. pp. 58-69.

Naseem, Usman, Razzak, Imran, and Eklund, Peter W. (2020) A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimedia Tools and Applications, 80. pp. 35239-35266.

Naseem, Usman, and Musial, Katarzyna (2019) DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis. In: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition. pp. 953-958. From: ICDAR 2019: 15th IAPR International Conference on Document Analysis and Recognition, 20-25 September 2019, Sydney, NSW, Australia.

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Poria, Soujanya, Chaturvedi, Iti, Cambria, Erik, and Bisio, Federica (2016) Sentic LDA: improving on LDA with semantic similarity for aspect-based sentiment analysis. In: Proceedings of the International Joint Conference on Neural Networks. pp. 4465-4473. From: IJCNN: 2016 International Joint Conference on Neural Networks, 24-29 July 2016, Vancouver, Canada.

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Rashid, Junaid, Kim, Jungeun, and Naseem, Usman (2023) Coherent Topic Modeling for Creative Multimodal Data on Social Media. In: Proceedings of the ACM Web Conference 2023. pp. 3923-3927. From: WWW 2023: The ACM Web Conference 2023, April 30 - May 4 2023, Austin, TX, USA.

Rashid, Junaid, Kim, Jungeun, and Naseem, Usman (2023) Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis. In: Proceedings of the ACM Web Conference 2023. pp. 73-76. From: WWW 2023: The ACM Web Conference 2023: Companion of The World Wide Web Conference, April 30 - May 4 2023, Austin, TX, USA.

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Satapathy, Ranjan, Guerreiro, Claudia, Chaturvedi, Iti, and Cambria, Erik (2017) Phonetic-based microtext normalization for Twitter sentiment analysis. In: Proceedings of the IEEE International Conference on Data Mining Workshops. pp. 407-413. From: ICDMW 2017: 17th IEEE International Conference on Data Mining Workshops, 18-21 November 2017, New Orleans, LA, USA.

Satapathy, Ranjan, Chaturvedi, Iti, Cambria, Erik, Ho, Shirley S., and Na, Jin Cheon (2017) Subjectivity detection in nuclear energy tweets. Computacion y Sistemas, 21 (4). pp. 657-664.

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Thapa, Surendrabikram, Rauniyar, Kritesh, Shiwakoti, Shuvam, Poudel, Sweta, Naseem, Usman, and Nasim, Mehwish (2023) NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse. In: Frontiers in Artificial Intelligence and Applications (372) pp. 2346-2353. From: ECAI 2020: 26th European Conference on Artificial Intelligence, 30 September - 4 October 2023, Kraków, Poland.

Thapa, Surendrabikram, Shah, Aditya, Jafri, Farhan Ahmad, Naseem, Usman, and Razzak, Imran (2022) A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict. In: Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text. From: CASE 2022: the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, 7-8 December 2022, Abu Dhabi, United Arab Emirates.

Thapa, Surendrabikram, Adhikari, Surabhi, Naseem, Usman, Singh, Priyanka, Bharathy, Gnana, and Prasad, Mukesh (2020) Detecting Alzheimer's Disease by Exploiting Linguistic Information from Nepali Transcript. In: Communications in Computer and Information Science (1332) pp. 176-184. From: ICONIP 202: 27th International Conference on Neural Information Processing, 18-22 November 2020, Bangkok, Thailand.

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Valdivia, Ana, Martínez-Cámara, Eugenio, Chaturvedi, Iti, Luzón, M. Victoria, Cambria, Erik, Ong, Yew Soon, and Herrera, Francisco (2020) What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules. Journal of Ambient Intelligence and Humanized Computing, 11. pp. 39-52.

Valdivia, Ana, Hrabova, Emiliya, Chaturvedi, Iti, Luzón, M. Victoria, Troiano, Luigi, Cambria, Erik, and Herrera, Francisco (2019) Inconsistencies on TripAdvisor reviews: a unified index between users and Sentiment Analysis Methods. Neurocomputing, 353. pp. 3-16.

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White, Nathan (2022) The Hmong Medical Corpus: a biomedical corpus for a minority language. Language Resources and Evaluation, 56. pp. 1315-1332.

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Xing, Frank, Schuller, Bjorn, Chaturvedi, Iti, Cambria, Erik, and Hussain, Amir (2023) Guest Editorial Neurosymbolic AI for Sentiment Analysis. IEEE Transactions on Affective Computing, 14 (3). pp. 1711-1715.

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Young, Tom, Cambria, Erik, Chaturvedi, Iti, Zhou, Hao, Biswas, Subham, and Huang, Minlie (2018) Augmenting end-to-end dialogue systems with commonsense knowledge. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. pp. 4970-4977. From: AAAI-18: 32nd AAAI Conference on Artificial Intelligence, 2-7 February 2018, New Orleans, LA, USA.