A Convolutional Neural Network for Aspect-Level Sentiment Classification

2019 ◽  
Vol 33 (14) ◽  
pp. 1959046 ◽  
Author(s):  
Yongping Xing ◽  
Chuangbai Xiao ◽  
Yifei Wu ◽  
Ziming Ding
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Extensive Literature ◽  
Effective Models

Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a sentence. Recurrent neural networks (RNNs) are regarded as effective models for handling NLP and have performed well in aspect-level sentiment classification. Extensive literature exists on sentiment classification that utilizes convolutional neural networks (CNNs); however, no literature on aspect-level sentiment classification that uses CNNs is available. In the present study, we develop a CNN model for handling aspect-level sentiment classification. In our model, attention-based input layers are incorporated into CNN to introduce aspect information. In our experiment, in which a benchmark dataset from Twitter is compared with other models, incorporating aspect information into CNN improves aspect-level sentiment classification performance without using syntactic parser or other language features.

scholarly journals Avaliação de Modelos de Redes Neurais Recorrentes para Anonimização de Textos em Português

2019 ◽  
Author(s):  
Antônio Franco ◽  
Leonardo Oliveira
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Automatic Generation ◽  
The Other ◽  
The Internet ◽  
Writing Style ◽  
Trade Off

Currently, there are several approaches to provide anonymity on the Internet. However, one can still identify anonymous users through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when accurately identify the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our results show a trade-off between the obfuscation level and the text semantics.

scholarly journals Tackling Graphical Natural Language Processing’s Problems with Recurrent Neural Networks

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Ali Sami Sosa ◽  
Saja Majeed Mohammed ◽  
Haider Hadi Abbas ◽  
Israa Al Barazanchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Structural Information ◽  
Sequential Data

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. Despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data; thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the Local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the Local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network.

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

scholarly journals Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
Elena Solovyeva ◽  
Ali Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Computational Cost ◽  
High Accuracy ◽  
Activation Functions ◽  
Fully Connected ◽  
Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

scholarly journals Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop

2019 ◽  
Vol 25 (4) ◽  
pp. 543-557 ◽  
Author(s):  
Afra Alishahi ◽  
Grzegorz Chrupała ◽  
Tal Linzen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Language Processing ◽  
Performance Testing ◽  
Network Architectures ◽  
Empirical Methods ◽  
Neural Models ◽  
The Impact ◽  
Systematic Manipulation

AbstractThe Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.

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