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Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

Xiang Li, Jian Su
2021 Security and Communication Networks  
sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that  ...  Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.  ...  Self-Attentive Depth Model for German Sentiment Analysis.  ... 
doi:10.1155/2021/8309586 fatcat:uump47t3cvcf5a3t25labxu3pu

Effective Attention Modeling for Aspect-Level Sentiment Classification

Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
2018 International Conference on Computational Linguistics  
Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target.  ...  We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016.  ...  Finally, we describe the overall architecture of our model for aspect-level sentiment classification and the training objective ( §3.5).  ... 
dblp:conf/coling/HeLND18 fatcat:6uqshtaok5ayzdfvsr2fakxaka

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.  ...  Zhou et al. 46 designed an attention-based LSTM network for cross-lingual sentiment classification at the document level.  ... 
arXiv:1801.07883v2 fatcat:nplicfgaozb6fbfx4eyts4zt7e

Enhanced Aspect Level Sentiment Classification with Auxiliary Memory

Peisong Zhu, Tieyun Qian
2018 International Conference on Computational Linguistics  
In our model, a main memory is used to capture the important context words for sentiment classification.  ...  In aspect level sentiment classification, there are two common tasks: to identify the sentiment of an aspect (category) or a term.  ...  Aspect level sentiment classification is arousing more and more researchers' attention, as it can provide more all-round and deeper analysis than document or sentence level sentiment classification.  ... 
dblp:conf/coling/ZhuQ18 fatcat:hwwzxgepzzanrn3qlkrenqc4sq

Enhancing Sentence Embedding with Generalized Pooling [article]

Qian Chen, Zhen-Hua Ling, Xiaodan Zhu
2018 arXiv   pre-print
We evaluate the proposed model on three different tasks: natural language inference (NLI), author profiling, and sentiment classification.  ...  We propose vector-based multi-head attention that includes the widely used max pooling, mean pooling, and scalar self-attention as special cases.  ...  Cheng et al. (2016) proposed an intra-sentence level attention mechanism on the base of LSTM, called LSTMN.  ... 
arXiv:1806.09828v1 fatcat:mqkw33awmzfnnhzdpoqgigloze

Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification [article]

Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, Xin Li, Qiang Yang
2018 arXiv   pre-print
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term  ...  In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same fine-grained level with the AT task.  ...  To capture phrase-level sentiment features in the context (e.g., "not satisfactory"), we employ a Bi-directional LSTM (Bi-LSTM) to preserve the contextual information for each word of the input sentence  ... 
arXiv:1811.10999v1 fatcat:4yggx6iisja2xloqjwwy4kfdo4

Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach [article]

Yao Qiang, Xin Li, Dongxiao Zhu
2020 arXiv   pre-print
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations.  ...  With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and  ...  Wang et al [28] applied attention and LSTM together in the model ATAE-LSTM by concatenating aspects with review word representations to compute the attention weights for aspect level sentiment classification  ... 
arXiv:2003.09986v1 fatcat:4nccxk7fxjfe3nm34c5uousnfy

Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification

Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, Xin Li
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term  ...  In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same finegrained level with the AT task.  ...  To capture phrase-level sentiment features in the context (e.g., "not satisfactory"), we employ a Bi-directional LSTM (Bi-LSTM) to preserve the contextual information for each word of the input sentence  ... 
doi:10.1609/aaai.v33i01.33014253 fatcat:6rgggwcaljbr5heynfkxgwm5vm

Comparative Analysis of Performance of Deep Learning Classification Approach based on LSTM-RNN for Textual and Image Datasets

Alaa Sahl Gaafar, Jasim Mohammed Dahr, Alaa Khalaf Hamoud
2022 Informatica (Ljubljana, Tiskana izd.)  
The outcomes reveal that the performance of the image-based deep learning model was better in terms of speed due to well-defined patterns of data representation against the data with sentiments-based deep  ...  While the LSTM-RNN with images offered better classification accuracy by 96.50% to 85.69% due to complex network architecture, processing elements, and features of the underlying datasets. Povzetek: .  ...  Hierarchical Graph Attention Network (HGAT) makes use of hierarchical attention architecture that is schema-level and node-level attention to recognize fake news of online news articles.  ... 
doi:10.31449/inf.v46i5.3872 dblp:journals/informaticaSI/GaafarDH22 fatcat:igza2z4vyfduxeaep2qcvxglle

Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis

Lishuang Li, Yang Liu, AnQiao Zhou
2018 Proceedings of the 22nd Conference on Computational Natural Language Learning  
On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words.  ...  Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important.  ...  We thank anonymous reviewers for their valuable comments.  ... 
doi:10.18653/v1/k18-1018 dblp:conf/conll/LiLZ18 fatcat:iapqzln6bzdkxoqsydxky5j4yy

FinEAS: Financial Embedding Analysis of Sentiment [article]

Asier Gutiérrez-Fandiño, Miquel Noguer i Alonso, Petter Kolm, Jordi Armengol-Estapé
2021 arXiv   pre-print
In this work, we propose a new model for financial sentiment analysis based on supervised fine-tuned sentence embeddings from a standard BERT model.  ...  In recent years, methods that use transfer learning from large Transformer-based language models like BERT, have achieved state-of-the-art results in text classification tasks, including sentiment analysis  ...  Conclusion and Future Work We have demonstrated that FinEAS, a model based on BERT pre-trained on the general domain but fine-tuned for sentence-level tasks, is a sensible approach for financial sentiment  ... 
arXiv:2111.00526v2 fatcat:mxot7zryfbhezed5itgysfa5mu

A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis

Sebastian Ruder, Parsa Ghaffari, John G. Breslin
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM.  ...  We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain  ...  Acknowledgments We thank the anonymous reviewers, Nicolas Pécheux, and Hugo Larochelle for their constructive feedback.  ... 
doi:10.18653/v1/d16-1103 dblp:conf/emnlp/RuderGB16 fatcat:dqxlsiugifeqfh5rxiiticgq2u

A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis [article]

Sebastian Ruder, Parsa Ghaffari, John G. Breslin
2016 arXiv   pre-print
We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM.  ...  We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain  ...  Acknowledgments We thank the anonymous reviewers, Nicolas Pécheux, and Hugo Larochelle for their constructive feedback.  ... 
arXiv:1609.02745v1 fatcat:tq7dyjoez5fghoznzlg6yxakzm

Aspect-Based Sentiment Classification with Attentive Neural Turing Machines

Qianren Mao, Jianxin Li, Senzhang Wang, Yuanning Zhang, Hao Peng, Min He, Lihong Wang
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
Experimental results verify that our model achieves state-of-the-art performance on aspect-based sentiment classification.  ...  Aspect-based sentiment classification aims to identify sentiment polarity expressed towards a given opinion target in a sentence.  ...  We also thank our anonymous reviewers for their constructive comments.  ... 
doi:10.24963/ijcai.2019/714 dblp:conf/ijcai/MaoLWZPHW19 fatcat:h6mqqrgunjgzxfdyplxqwr75bu

NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification

Alexandra Chronopoulou, Aikaterini Margatina, Christos Baziotis, Alexandros Potamianos
2018 Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis  
Our models are based on LSTM networks, augmented with a selfattention mechanism. We use the weights of various pretrained models, for initializing specific layers of our networks.  ...  We leverage a big collection of unlabeled Twitter messages, for pretraining word2vec word embeddings and a set of diverse language models.  ...  For all our models, we employ the same 2-layer attention-based LSTM ar- Table 1 : Hyper-parameters of our models.  ... 
doi:10.18653/v1/w18-6209 dblp:conf/wassa/ChronopoulouMBP18 fatcat:whpdda5i4ba2rgyzlcjccbxgqi
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