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Token-wise sentiment decomposition for ConvNet: Visualizing a sentiment classifier

Piyush Chawla, Subhashis Hazarika, Han-Wei Shen
2020 Visual Informatics  
(Piyush Chawla), hazarika.3@osu.edu (Subhashis Hazarika), shen.94@osu.edu (Han-Wei Shen) 1 https://ai.stanford.edu/~amaas/data/sentiment/ J o u r n a l P r e -p r o o f Journal Pre-proof J o u r n a l  ...  In this work, we present a visualization technique that can be used to understand the inner workings of text-based CNN models.  ...  We leave that for future work. We plan to extend our analysis approach to other settings like medical text classification etc.  ... 
doi:10.1016/j.visinf.2020.04.006 fatcat:rdp5khtxczfqvgjjnkn2pfh4tu

A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks [article]

Israa Khalaf Salman Al-Tameemi, Mohammad-Reza Feizi-Derakhshi, Saeed Pashazadeh, Mohammad Asadpour
2022 arXiv   pre-print
To serve as a resource for academics in this rapidly growing field, we introduce a comprehensive overview of textual and visual SA, including data pre-processing, feature extraction techniques, sentiment  ...  Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions.  ...  Secondly, a deep Tucker fusion approach was proposed for visual-textual SA using Tucker decomposition and bilinear pooling operation to integrate deep visual and textual representations. J. Xu et al.  ... 
arXiv:2207.02160v1 fatcat:l3vxpjnqkrfthkvhdldwonpoe4

A Survey on Green Deep Learning [article]

Jingjing Xu, Wangchunshu Zhou, Zhiyi Fu, Hao Zhou, Lei Li
2021 arXiv   pre-print
For each category, we discuss the progress that has been achieved and the unresolved challenges.  ...  This paper focuses on presenting a systematic review of the development of Green deep learning technologies.  ...  In this way, we transfer sentiment prediction to a masked language modeling task.  ... 
arXiv:2111.05193v2 fatcat:t2blz24y2jakteeeawqqogbkpy

International Research Conference on Smart Computing and Systems Engineering SCSE 2020 Proceedings [Full Conference Proceedings]

2020 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)  
ACKNOWLEDGMENT The authors would like to thank the Department of Census and Department of Irrigation, Sri Lanka for providing the paddy yield and climate data for this study.  ...  would like to extend their heartfelt gratitude and acknowledgment to all the language experts, Sinhala teachers from different schools and especially, the Sinhala Department in the University of Kelaniya for  ...  Convolutional Neural Networks (CNN or ConvNet) [4] is a well-known deep learning algorithm. It was invented based on the natural visual perception mechanism of the living creatures.  ... 
doi:10.1109/scse49731.2020.9313027 fatcat:gjk5az2mprgvrpallwh6uhvlfi

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Michael Mesfin Tadesse, Hongfei Lin, Bo Xu, Liang Yang
2019 Algorithms  
Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.  ...  For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models.  ...  Acknowledgments: The authors would like to thank Janka Koperdanova for her full support and editing of the paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a13010007 fatcat:7w2sevyfejfobdltrzibwkl4oq

Text Classification Algorithms: A Survey

Kowsari, Jafari Meimandi, Heidarysafa, Mendu, Barnes, Brown
2019 Information  
However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers.  ...  In thispaper, a brief overview of text classification algorithms is discussed.  ...  Gerber for his feedback and comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info10040150 fatcat:qfmjtzsaoreahdwdwlfhymjtru

Explainable AI: A Review of Machine Learning Interpretability Methods

Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
2020 Entropy  
, in the hope that this survey would serve as a reference point for both theorists and practitioners.  ...  This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare.  ...  Layer-wise Relevance Propagation (LRP) [37] is a "decomposition of nonlinear classifiers" technique that brings interpretability to highly complex deep neural networks by propagating their predictions  ... 
doi:10.3390/e23010018 pmid:33375658 pmcid:PMC7824368 fatcat:gv42gcovm5cxzl2kmdsluiegdi

Computational Socioeconomics [article]

Jian Gao, Yi-Cheng Zhang, Tao Zhou
2019 arXiv   pre-print
Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development.  ...  In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools  ...  Pak and Paroubek [643] performed a linguistic analysis of tweets and trained a sentiment classifier to determine positive, neutral and negative sentiments for a document.  ... 
arXiv:1905.06166v1 fatcat:kvhy2hpzgvg2vnqhdjfyjfidqi

Automated Mental Disorders Assessment Using Machine Learning

Niloufar Abaei Koupaei, University, My
2021
The dataset comprises audio-visual recordings of bipolar disorder patients undergoing a structured interview. We propose three bipolar disorder classification solutions.  ...  Although the symptoms for different mental disorders vary, they generally are characterized by a combination of abnormal behaviours, thoughts, and emotions.  ...  We presented a possible vision for such system in Chapter 1.  ... 
doi:10.20381/ruor-27231 fatcat:pw6rcqdk2femrmfa2w5zktb6si

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [article]

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 arXiv   pre-print
Then, we outline a few important applications of UQ methods.  ...  It can be applied to solve a variety of real-world applications in science and engineering.  ...  [447] presented a probabilistic approach for solving the task of 'Visual Dialog'.  ... 
arXiv:2011.06225v4 fatcat:wwnl7duqwbcqbavat225jkns5u

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
They have been applied to solve a variety of real-world problems in science and engineering.  ...  Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes.  ...  2020 CamVid Semantic N/A N/A CNN Functional VI × segmentation and pixel-wise depth regression Angelopoulos et 2020 ImageNet and Image classifiers N/A N/A CNN Conformal prediction √ al. [660] ImageNet-V2  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods

Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow
2021 The Journal of Artificial Intelligence Research  
Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video.  ...  Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks.  ...  We extend our special thanks to Matthew Kuhn and Stephanie Lund for painstakingly proofing the whole manuscript.  ... 
doi:10.1613/jair.1.11688 fatcat:kvfdrg3bwrh35fns4z67adqp6i

Analysis and Application of Language Models to Human-Generated Textual Content

Marco Di Giovanni
2022
Tested tasks include the extraction of emerging knowledge, represented by users similar to a given set of well-known accounts, controversy detection, obtaining controversy scores for topics discussed online  ...  I selected Twitter as the principal source of data for the performed experiments since its users mainly share short and noisy texts.  ...  I visualized their distribution and how the sentiment of posts changed over time.  ... 
doi:10.48676/unibo/amsdottorato/10057 fatcat:gbjtww6jabcoddn5spdpx4z4dq

Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods [article]

Aditya Mogadala and Marimuthu Kalimuthu and Dietrich Klakow
2020 arXiv   pre-print
Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video.  ...  The largest of the growths in these fields has been made possible with deep learning, a sub-area of machine learning, which uses the principles of artificial neural networks.  ...  We extend our special thanks to Matthew Kuhn and Stephanie Lund for painstakingly proofing the whole manuscript.  ... 
arXiv:1907.09358v2 fatcat:4fyf6kscy5dfbewll3zs7yzsuq

Training dynamics of neural language models [article]

Naomi Saphra, University Of Edinburgh, Adam Lopez, Timothy Hospedales
2021
We instead use mathematical tools designed for investigating language model training dynamics.  ...  For decades, linguists have used the tools of developmental linguistics to study human bias towards linguistic structure.  ...  For example, it might modify a neuron associated with particular inputs like parentheses , or properties like sentiment (Radford et al., 2017) . Representations of Target Words.  ... 
doi:10.7488/era/1421 fatcat:adqneqxil5eijesi4mkkk3uh4y
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