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Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models [article]

Xinzhi Wang, Shengcheng Yuan, Hui Zhang, Yi Liu
2018 arXiv   pre-print
Two deep neural network models are presented for sentiment calculation.  ...  Although neural network models have contributed a lot to mining text information, little attention is paid to analysis of the inter-sentiment correlations.  ...  Three kinds of features and two deep neural network models are proposed and applied to three datasets. The two deep neural network models are presented for sentiment calculation.  ... 
arXiv:1811.09755v1 fatcat:uc5mgxoxj5b77ftari45m4tb4u

A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

Khaoula Mrhar, Lamia Benhiba, Samir Bourekkache, Mounia Abik
2021 International Journal of Emerging Technologies in Learning (iJET)  
To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task.  ...  In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM.  ...  In addition, Hassan et al. proposed a ConvLstm neural network architecture that employs both convolutional and recurrent layers on top of a pre-trained word vector [12] .  ... 
doi:10.3991/ijet.v16i23.24457 fatcat:2rgghpj6avdbbnwsn6qb3yjezq

Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis [article]

Sunny Verma, Jiwei Wang, Zhefeng Ge, Rujia Shen, Fan Jin, Yang Wang, Fang Chen, Wei Liu
2020 arXiv   pre-print
We then integrate these two kinds of information via a fusion layer and call our novel multimodal fusion scheme as Deep-HOSeq (Deep network with higher order Common and Unique Sequence information).  ...  In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks.  ...  We extensively evaluate the performance of our proposed Deep-HOSeq against neural-based and non-neural based schemes available for multimodal sentiment analysis.  ... 
arXiv:2010.08218v1 fatcat:kga5e7lerzfztatzstqujr24q4

Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis [article]

Jiahao Zheng, Sen Zhang, Xiaoping Wang, Zhigang Zeng
2022 arXiv   pre-print
Experimental results on two public datasets demonstrate the effectiveness of the proposed model.  ...  Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression.  ...  Acknowledgments The work was supported by the National Natural Science Foundation of China under Grant no. 61876209 and the Na-  ... 
arXiv:2201.03969v1 fatcat:vmsrtedszbgovbhvakfhljpgye

Targeted Sentiments and Hierarchical Events based Learning Model for Stock Market Forecasting from Multi-Source Data

The forecasting results obtained by using the TFMA-DRL model by combining the stock indicators of targeted sentiments and hierarchical events are trustworthy and reliable.  ...  Studies were focussed on extracting the events and sentiments from different source data and employ them in learning the stock price movement patterns.  ...  [15] also used a deep learning model of convolution neural network (CNN) for stock forecasting. Kelotra and Pandey [16] introduced optimized deep-convolution LSTM for stock estimation.  ... 
doi:10.35940/ijitee.f4170.049620 fatcat:x7uqgu3mlzb4dno7s4cbdonq2y

Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis [article]

Ziad Al-Halah, Andrew Aitken, Wenzhe Shi, Jose Caballero
2020 arXiv   pre-print
Hence, we construct a novel dataset of 4 million images collected from Twitter with their associated emojis. We train a deep neural model for image embedding using emoji prediction task as a proxy.  ...  Furthermore, without bell and whistles, our compact, effective and simple embedding outperforms the more elaborate and customized state-of-the-art deep models on these public benchmarks.  ...  of large-scale datasets suited for advanced models like deep neural networks.  ... 
arXiv:1907.06160v3 fatcat:sxfq4z5ggvb5jf3x4frid7wwfu

Efficacy of Deep Neural Embeddings based Semantic Similarity in Automatic Essay Evaluation

Manik Hendre, Prasenjit Mukherjee, Raman Preet, Manish Godse
2021 International Journal of Computing and Digital Systems  
Deep neural embeddings are widely used in natural language processing (NLP) applications like question answering, prediction of next word or sentence, translation of language, word sense disambiguation  ...  Correlation of semantic similarity scores with different essay-specific traits given in the ASAP++ dataset is also performed.  ...  Recent advances in the artificial neural network, including deep learning, have inducted new methods like word embedding and Word2vec.  ... 
doi:10.12785/ijcds/1001122 fatcat:4uctslbreveulca5q2qm766ewq

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.  ...  Zadeh et al. 136 formulated the problem of multimodal sentiment analysis as modelling intra-modality and inter-modality dynamics and introduced a new neural model named Tensor Fusion Network to tackle  ... 
arXiv:1801.07883v2 fatcat:nplicfgaozb6fbfx4eyts4zt7e

Quantum-inspired Multimodal Fusion for Video Sentiment Analysis [article]

Qiuchi Li, Dimitris Gkoumas, Christina Lioma, Massimo Melucci
2021 arXiv   pre-print
The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets.  ...  Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner.  ...  superposition and mixture to model correlations between linguistic features and construct complex-valued language representations by neural networks.  ... 
arXiv:2103.10572v2 fatcat:hldyp5i35jhwrdtc7gvneb353m

Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing [article]

Hanlu Wu, Tengfei Ma, Lingfei Wu, Shouling Ji
2021 arXiv   pre-print
We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels.  ...  Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks.  ...  This demonstrates the effectiveness of capturing inter-worker and inter-task latent correlations.  ... 
arXiv:2010.13080v2 fatcat:zwepjyziyfakxmo5fwlg3wxjxy

Sentiment and Sarcasm Classification with Multitask Learning [article]

Navonil Majumder, Soujanya Poria, Haiyun Peng, Niyati Chhaya, Erik Cambria, Alexander Gelbukh
2019 arXiv   pre-print
We show that these two tasks are correlated, and present a multi-task learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multi-task  ...  Our method outperforms the state of the art by 3-4% in the benchmark dataset.  ...  Inter-Task Communication We use neural tensor network (NTN) of size Dntn = 100 to fuse sarcasm-and sentiment-specific sentence representations, ssar and ssen, to obtain the fused representation s+, where  ... 
arXiv:1901.08014v2 fatcat:sgiymzqbhzfv3gfrzwrzp7u7im

Emotion Correlation Mining Through Deep Learning Models on Natural Language Text [article]

Xinzhi Wang, Luyao Kou, Vijayan Sugumaran, Xiangfeng Luo, Hui Zhang
2020 arXiv   pre-print
To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural network models are presented.  ...  Correlation among emotions contributes to the failure of emotion recognition.  ...  Emotion Classification Model Here, two deep neural-network models, CNN-LSTM2 (M1) and CNN-LSTM2-STACK (M2), are employed for emotion recognition.  ... 
arXiv:2007.14071v1 fatcat:edhzq6ldkjevddnum5mxcoqm3q

Related Tasks can Share! A Multi-task Framework for Affective language [article]

Kumar Shikhar Deep, Md Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya
2020 arXiv   pre-print
Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity.  ...  Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set.  ...  ), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).  ... 
arXiv:2002.02154v1 fatcat:6amgb52bajhtzda2wppivate7i


Parvati Kadli, Dr. Vidyavathi B M
2021 Indian Journal of Computer Science and Engineering  
This work proposes a deep learned novel architecture named Integrated Polarity Score based Pattern Embedding on Tri Model Attention (IPSPE_TMA) Network which is a model based on Bidirectional Long-short  ...  This network implementation for sentiment analysis on cross domain dataset gives better performance than many of the previous works.  ...  Fusion of all network models with its attention scheme produces deep features which is robust to cross domain sentiment analysis.  ... 
doi:10.21817/indjcse/2021/v12i6/211206190 fatcat:jqhebsw5nbg4jkmowgjzdnvi4u

Sentiment analysis by deep learning approaches

Sreevidya P., O. V. Ramana Murthy, S. Veni
2020 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes.  ...  Performance measures-Accuracy, precision, recall and F1-score-are observed to outperformthe existing models.  ...  A combined audio and text model was developed by implementing deep neural networks.  ... 
doi:10.12928/telkomnika.v18i2.13912 fatcat:5s5ghjwwv5eclf23yzyg73evby
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