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ScenarioSA: A Publicly Available Conversational Database for Interactive Sentiment Analysis
2020
IEEE Access
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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
2021
Australasian Journal of Educational Technology
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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]
2018
arXiv
pre-print
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2018 pre-print arXiv:1811.09362v1 fatcat:sg2t7hp3vfbtfiwe62ikoczhpu
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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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
2020
AAAI Conference on Artificial Intelligence
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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]
2018
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2019
Applied Sciences
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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
2019
2019 International Joint Conference on Neural Networks (IJCNN)
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2021 pre-print arXiv:2103.03755v1 fatcat:ypveqsednva2hph5qcmrjm7k4m
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
2022
Wireless Communications and Mobile Computing
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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
2019
Science China Information Sciences
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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
2019
Proceedings of the 2019 Conference of the North
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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
2017
Proceedings of the 2017 ACM on Multimedia Conference - MM '17
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
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