Filters








1,296 Hits in 4.7 sec

ScenarioSA: A Publicly Available Conversational Database for Interactive Sentiment Analysis

Yazhou Zhang, Zhipeng Zhao, Panpan Wang, Xiang Li, Lu Rong, Dawei Song
2020 IEEE Access  
In this paper, we present a new conversational database that we have created and made publicly available, namely ScenarioSA, for interactive sentiment analysis.  ...  Interactive sentiment analysis is an emerging, yet challenging, subtask of the natural language processing problem.  ...  attention networks (IAN), and an improved interactive attention networks with influence (IAN-INF) that incorporates three learned influence matrices into the output gate of each LSTM unit for obtaining  ... 
doi:10.1109/access.2020.2994147 fatcat:awrn47lqs5bvpdrcmt23tixpky

Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis

Changqin Huang, Zhongmei Han, Ming Li, Xizhe Wang, Wenzhu Zhao
2021 Australasian Journal of Educational Technology  
Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning  ...  The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts.  ...  an optimal algorithm for classifying sentiments and interactions.  ... 
doi:10.14742/ajet.6749 fatcat:7k26fhdpevcevk5c2amb6njg4e

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors [article]

Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
2018 arXiv   pre-print
Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition.  ...  Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions.  ...  We also thank the anonymous reviewers for useful feedback.  ... 
arXiv:1811.09362v2 fatcat:t6lih6egejcwvgdjzli5jnmyda

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition.  ...  Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions.  ...  We also thank the anonymous reviewers for useful feedback.  ... 
doi:10.1609/aaai.v33i01.33017216 fatcat:cx22rdjwbncf7hpqar6fif6uqe

Editorial for the 3rd Workshop on Affective Content Analysis (AffCon) at AAAI 2020

Niyati Chhaya, Kokil Jaidka, Jennifer Healey, Lyle Ungar, Atanu R. Sinha
2020 AAAI Conference on Artificial Intelligence  
Acknowledgments We want to thank Adobe Research for their generous funding, which made this workshop possible. We thank our program committee members who did an ex-  ...  The method leveraged transfer learning to fine-tune a pre-trained dialog model with human feedback using reinforcement learning, and shows how learning from cues like a user's sentiment is more effective  ...  An automated analysis that studied the interactions and dependency patterns in support groups was discussed.  ... 
dblp:conf/aaai/ChhayaJHUS20 fatcat:65k3bapbavcdxazavbmzzurevu

Multimodal Relational Tensor Network for Sentiment and Emotion Classification [article]

Saurav Sahay, Shachi H Kumar, Rui Xia, Jonathan Huang, Lama Nachman
2018 arXiv   pre-print
We present the results of our model on CMU-MOSEI dataset and show that our model outperforms many baselines and state of the art methods for sentiment classification and emotion recognition.  ...  the inter-segment inter-modal interactions.  ...  This LSTM model outperforms the SVM multimodal baseline by almost 5% binary class accuracy scores for sentiment analysis.  ... 
arXiv:1806.02923v1 fatcat:sohdzj7qejatnhnilvtlts7yaq

Deep Learning for Sentiment Analysis : A Survey [article]

Lei Zhang, Shuai Wang, Bing Liu
2018 arXiv   pre-print
Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years.  ...  This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.  ...  Ltd with a research gift.  ... 
arXiv:1801.07883v2 fatcat:nplicfgaozb6fbfx4eyts4zt7e

Incorporating Word Significance into Aspect-Level Sentiment Analysis

Refuoe Mokhosi, ZhiGuang Qin, Qiao Liu, Casper Shikali
2019 Applied Sciences  
analysis with a decay factor β = 0.7 .  ...  Aspect-level sentiment analysis has drawn growing attention in recent years, with higher performance achieved through the attention mechanism.  ...  Acknowledgments: We thank the anonymous reviewers for their helpful and insightful advice. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9173522 fatcat:fhvklnnhbzd5fbsos2wkvo5cvm

Leveraging Recursive Processing for Neural-Symbolic Affect-Target Associations

Alexander Sutherland, Sven Magg, Stefan Wermter
2019 2019 International Joint Conference on Neural Networks (IJCNN)  
associations in an aspect-based sentiment analysis task.  ...  Explaining the outcome of deep learning decisions based on affect is challenging but necessary if we expect social companion robots to interact with users on an emotional level.  ...  As such, the Tree-LSTM had an input of size 300, as dictated by the Common Crawl 840B pretrained embedding, a hidden layer of size 168 and an output layer of size 3 with softmax applied for classification  ... 
doi:10.1109/ijcnn.2019.8851875 dblp:conf/ijcnn/SutherlandMW19 fatcat:cf252rvudvcpfner2x5qkzuyq4

Multimodal Sentiment Analysis Based on Interactive Transformer and Soft Mapping

Zuhe Li, Qingbing Guo, Chengyao Feng, Lujuan Deng, Qiuwen Zhang, Jianwei Zhang, Fengqin Wang, Qian Sun
2022 Wireless Communications and Mobile Computing  
In this paper, we propose an Interactive Transformer and Soft Mapping based method for multimodal sentiment analysis.  ...  The proposed model can fully consider the relationship between multiple modal pieces of information and provides a new solution to the problem of data interaction in multimodal sentiment analysis.  ...  For example, in customer sentiment analysis, multimodal conversational sentiment analysis is used to obtain interaction clues among multiple customers and to predict sentiment evolution trend in the interaction  ... 
doi:10.1155/2022/6243347 doaj:703bcf36ed9c4917ab9f5c467131ddea fatcat:7l4h3uhjffcbxpcjfj4mtaf6h4

Sentiment analysis using deep learning approaches: an overview

Olivier Habimana, Yuhua Li, Ruixuan Li, Xiwu Gu, Ge Yu
2019 Science China Information Sciences  
Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of data in an enormous and dynamic way.  ...  However, the traditional approaches for sentiment analysis are accused of being inefficient to cope with the new trend of data with dynamic nature of language, increase of high dimensional data, structural  ...  Attention based models with aspect information. Wang et al. [140] designed an attention-based LSTM with aspect embedding (ATAE-LSTM) model for aspect sentiment analysis.  ... 
doi:10.1007/s11432-018-9941-6 fatcat:nbevrfiyybhszirol2af26c6ve

A Dynamic Speaker Model for Conversational Interactions

Hao Cheng, Hao Fang, Mari Ostendorf
2019 Proceedings of the 2019 Conference of the North  
In this work, we introduce a neural model for learning a dynamically updated speaker embedding in a conversational context.  ...  Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations.  ...  Acknowledgements We thank the anonymous reviewers for their helpful feedback. We also thank Trang Tran for her feedback on the paper.  ... 
doi:10.18653/v1/n19-1284 dblp:conf/naacl/00020O19 fatcat:ivlqmevaubahfbsm2kocpj4tpe

Temporally Selective Attention Model for Social and Affective State Recognition in Multimedia Content

Hongliang Yu, Liangke Gui, Michael Madaio, Amy Ogan, Justine Cassell, Louis-Philippe Morency
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
Extensive experiments show that our model achieves the state-of-the-art performance on rapport detection and multimodal sentiment analysis.  ...  Our TSAM models learn to recognize affective and social states using a new loss function called speaker-distribution loss.  ...  Better understanding of the social dynamics during these remote interactions has the potential to increase engagement and learning gains [55] .  ... 
doi:10.1145/3123266.3123413 dblp:conf/mm/YuGMOCM17 fatcat:265m3fyyfrdkzl6kxx7hmbk55y

Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis [article]

Yequan Wang, Xuying Meng, Yiyi Liu, Aixin Sun, Yao Wang, Yinhe Zheng, Minlie Huang
2022 arXiv   pre-print
Many studies on dialog emotion analysis focus on utterance-level emotion only.  ...  Emotion curve refers to the change of emotions along the development of a conversation.  ...  One capsule is built for one sentiment category, and each capsule is built with an attribute, a state, and three modules.  ... 
arXiv:2203.12254v1 fatcat:iehqcfggtzfsxo2s2osno7vlxi

A Review on Text-Based Emotion Detection – Techniques, Applications, Datasets, and Future Directions [article]

Sheetal Kusal, Shruti Patil, Jyoti Choudrie, Ketan Kotecha, Deepali Vora, Ilias Pappas
2022 arXiv   pre-print
Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions.  ...  An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.  ...  modalities (Pleasantness), interaction contents (Attention), interaction dynamics (Sensitivity), interaction benefits (Aptitude).  ... 
arXiv:2205.03235v1 fatcat:b3m25fg6xfc3leeym22eqysq5a
« Previous Showing results 1 — 15 out of 1,296 results