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Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification [article]

Binxuan Huang, Kathleen M. Carley
2019 arXiv   pre-print
We introduce a novel parameterized convolutional neural network for aspect level sentiment classification.  ...  Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN).  ...  Acknowledgments We would like to thank the reviewers for their helpful comments that greatly improved the article. We would also like to thank Sumeet Kumar for his valuable suggestions.  ... 
arXiv:1909.06276v1 fatcat:ezmlvrsgqfhv7cz7ciblxyobva

Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification

Binxuan Huang, Kathleen Carley
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
We introduce a novel parameterized convolutional neural network for aspect level sentiment classification.  ...  Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN).  ...  Acknowledgments We would like to thank the reviewers for their helpful comments that greatly improved the article. We would also like to thank Sumeet Kumar for his valuable suggestions.  ... 
doi:10.18653/v1/d18-1136 dblp:conf/emnlp/HuangC18 fatcat:cueaxzqtvfefrcrhyic7rpkt4q

Object Detection Based on Faster R-Cnn

M. Sushma Sri, B. Rajendra Naik, K. Jaya Sankar
2021 International Journal of Engineering and Advanced Technology  
In this article, the author proposes Object detection with deep neural networks and faster region convolutional neural networks methods for providing a simple algorithm which provides better accuracy and  ...  In this regard as there is rapid development in Deep Learning for its high-level processing, extracting deeper features, reliable and flexible compared to conventional techniques.  ...  The addition of more layers in convolutional neural networks is met for features extraction and as region are proposed to this convolutional neural network can be deployed at low cost and generates better  ... 
doi:10.35940/ijeat.c2186.0210321 fatcat:4khk6nm6jra4ncgl2m73ufcztq

Neural Networks for Sentiment Analysis in Czech

Ladislav Lenc, Tomás Hercig
2016 Conference on Theory and Practice of Information Technologies  
This paper presents the first attempt at using neural networks for sentiment analysis in Czech.  ...  We first perform experiments on two English corpora to allow comparability with the existing state-ofthe-art methods for sentiment analysis in English.  ...  SGS-2016-018 Data and Software Engineering for Advanced Applications.  ... 
dblp:conf/itat/LencH16 fatcat:sejhxyfsxveyrpmgylfln4e5mq

Deep Convolutional Neural Network for Pedestrian Detection with Multi-Levels Features Fusion

Danhua Li, Xiaofeng Di, Xuan Qu, Yunfei Zhao, Honggang Kong, Yansong Wang
2018 MATEC Web of Conferences  
The current state-of-the-art method is Faster RCNN, which is such a network that uses a region proposal network (RPN) to generate high quality region proposals, while Fast RCNN is used to classifiers extract  ...  The contribution of this paper is integrated low-level features and high-level features into a Faster RCNN-based pedestrian detection framework, which efficiently increase the capacity of the feature.  ...  For future work, we want to explore the fusion of man-made features into convolutional neural network to make it even strong.  ... 
doi:10.1051/matecconf/201823201061 fatcat:twe2v3tupzgn5nzcf5a3wfzm5m

Aspect based Sentiment Analysis with Feature Enhanced Attention CNN-BiLSTM

Wei Meng, Yongqing Wei, Peiyu Liu, Zhenfang Zhu, Hongxia Yin
2019 IEEE Access  
This paper introduces an aspect level neural network for sentiment analysis named Feature Enhanced Attention CNN-BiLSTM (FEA-NN).  ...  Besides, the feature extraction ability of the model is also essential for effective analysis, the combination of CNN and LSTM can enhance the feature extraction ability and semantic expression ability  ...  The basic structure of a convolutional neural network includes convolution layer, pooling layer, and fully connected layer. 1) CONVOLUTIONAL LAYER The purpose of the convolutional layer is to extract  ... 
doi:10.1109/access.2019.2952888 fatcat:37h7hgmbwnfflh35ojk2tmtghy

Aspect-Based Sentiment Analysis Using Hybrid CNN-SVM with Particle Swarm Optimization for Domain Independent Datasets

2020 International Journal of Emerging Trends in Engineering Research  
In this paper, we suggested novel intelligent framework based on hybrid convolutional neural network and support vector machine (SVM) for aspect-based sentiment detection and classification of online product  ...  Particularly convolutional neural network (CNN) has fascinated extensive attention since its amazing functioning in several applications including text analytics.  ...  The convolutional neural network (CNN) has been employed for the extraction of aspect-based sentiment features. 3.  ... 
doi:10.30534/ijeter/2020/628102020 fatcat:bixgm5d7fvbqraizwbc73a3q3q

Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

Junhao Yang, Chunxiao Chen, Qingyang Zang, Jianfei Li
2018 MCB Molecular and Cellular Biomechanics  
To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor.  ...  Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with  ...  Image Recognition of Breast Tumor Proliferation Level  ... 
doi:10.32604/mcb.2018.03824 fatcat:sdd4yvy2dbdstleujzmlksdz44

Convolutional Neural Network Model in Machine Learning Methods and Computer Vision for Image Recognition: A Review

2018 Journal of Applied Sciences Research  
In CNNs, the weights of the convolutional layer being used for feature extraction in addition to the fully connected layer are applied for classification that are determined during the training process  ...  There are a number of reasons that convolutional neural networks (CNNs) are becoming important. Feature extractors are hand designed during traditional models for image recognition.  ...  Convolutional Neural Networks.  ... 
doi:10.22587/jasr.2018.14.6.5 fatcat:stw6qs54szbkbozd2yy3edch6y

Aspect-based Sentiment Summarization with Deep Neural Networks

Dhanush D, Abhinav Kumar Thakur, Narasimha Prasad Diwakar
2016 International Journal of Engineering Research and  
The proposed design consists of separate models for aspect extraction by tagging aspects in a sentence using Recurrent Neural Network and sentence level sentiment classification using Convolution Neural  ...  ASBA can be used for Summarization of reviews in e-commerce sites, blogs, discussion forums, etc. The problem under ASBA has two major tasks which are aspect extraction and sentiment classification.  ...  One of the implementation of ASBA using deep learning framework was using cascaded Convolution Neural Networks were used for aspect extraction and sentiment classification [4] .  ... 
doi:10.17577/ijertv5is050553 fatcat:grauqnl3ivg6hgpqptpxje4eiu

MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews

Wei Xue, Wubai Zhou, Tao Li, Qing Wang
2017 International Joint Conference on Natural Language Processing  
In this paper, we propose a multi-task learning model based on neural networks to solve them together.  ...  In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated reviews.  ...  for Aspect classification and extraction) that interleaves the two tasks.  ... 
dblp:conf/ijcnlp/XueZLW17 fatcat:ychvxstgq5hbvlmft3ou3bifya

3D Object Classification Based on Multi Convolutional Neural Networks

Mei-qi LU, Wei LI, Ya-guang NING
2017 DEStech Transactions on Engineering and Technology Research  
A 3D object classification approach based on multi convolutional neural networks is presented in this paper.  ...  by applying both methods. (2) Different from traditional methods, CNN (Convolutional Neural Network) was used to extract features from both color image and depth image. (3) Consider on the relation-features  ...  Convolutional neural network for 3D object classification.  ... 
doi:10.12783/dtetr/amma2017/13362 fatcat:fedh6qdevbb77lw7fu6jt3bz7e

Aspect-based Opinion Summarization with Convolutional Neural Networks [article]

Haibing Wu, Yiwei Gu, Shangdi Sun, Xiaodong Gu
2015 arXiv   pre-print
To tackle aspect mapping and sentiment classification, we propose two Convolutional Neural Network (CNN) based methods, cascaded CNN and multitask CNN.  ...  Cascaded CNN contains two levels of convolutional networks. Multiple CNNs at level 1 deal with aspect mapping task, and a single CNN at level 2 deals with sentiment classification.  ...  The first one is a two-level Cascaded CNN (C-CNN). At level 1, multiple convolutional networks map the input sentences into pre-defined aspects.  ... 
arXiv:1511.09128v1 fatcat:qtehmu2ez5dllko7njah76rlta

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Lin Wang, Zuqiang Meng
2022 Sensors  
Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales.  ...  To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis.  ...  Multichannel Two-Dimensional Convolutional Neural Networks In this subsection, a multichannel two-dimensional convolutional neural network is used for sentiment analysis, and each two-dimensional convolution  ... 
doi:10.3390/s22030714 pmid:35161459 pmcid:PMC8840113 fatcat:x4otgcpvtzaaxm6q6wb3kvhwsa

Brain Image Recognition Algorithm and High Performance Computing of Internet of Medical Things Based on Convolutional neural network

Yuxi Liu, Jun Xiong
2019 IEEE Access  
Aiming at the shortcomings of traditional medical image recognition methods, this paper proposes an adaptive convolutional neural network model CNN-BN-PReLU based on the convolutional neural network method  ...  INDEX TERMS Brain image recognition, high performance computing, convolutional neural network.  ...  There are two main aspects in the various high-level descriptions of convolutional neural network mentioned above: (1) The model consists of multi-level or stage nonlinear information processing; (2) Supervised  ... 
doi:10.1109/access.2019.2933206 fatcat:w6wxml5l45f4ho3b2gw5lldpee
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