Sentiment Classification Using Convolutional Neural Networks

Kim, Hannah; Jeong, Young Seob

doi:10.3390/app9112347

Cited by 140 publications

(78 citation statements)

References 42 publications

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“…The reason for the model is the parallel work to extract the more important features. Consecutive parallel layers improved the classification accuracy [44]. Table 1 shows the complete details about the pool size and the number of kernels in our proposed model.…”

Section: Methodsmentioning

confidence: 99%

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

et al. 2020

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Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

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Section: Methodsmentioning

confidence: 99%

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

et al. 2020

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“…Then, we used CTI to dynamically combine the target-context information with the context word. Finally, we used Convolutional Neural Networks (CNN) [22,23] to extract features for classification.…”

Section: Related Workmentioning

confidence: 99%

Multiple Interactive Attention Networks for Aspect-Based Sentiment Classification

Zhang

Zhu

et al. 2020

Applied Sciences

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Aspect-Based (also known as aspect-level) Sentiment Classification (ABSC) aims at determining the sentimental tendency of a particular target in a sentence. With the successful application of the attention network in multiple fields, attention-based ABSC has aroused great interest. However, most of the previous methods are difficult to parallelize, insufficiently obtain, and fuse the interactive information. In this paper, we proposed a Multiple Interactive Attention Network (MIN). First, we used the Bidirectional Encoder Representations from Transformers (BERT) model to pre-process the data. Then, we used the partial transformer to obtain a hidden state in parallel. Finally, we took the target word and the context word as the core to obtain and fuse the interactive information. Experimental results on the different datasets showed that our model was much more effective.Appl. Sci. 2020, 10, 2052 2 of 15 words [7]. However, these models simply average the aspect or context vector to guide learning the attention weight on the context or aspect words. Therefore, these models are still in the preliminary stage in dealing with fine-grained sentiment analysis.In conclusion, there are two problems with previous approaches. The first problem is that previous approaches are difficult to obtain the hidden state interactively in parallel. Another problem is to insufficiently obtain and fuse contextual information and aspect information.This paper proposed a model named Multiple Interactive Attention Network (MIN) to address these problems. To address the first problem, we took advantage of Multi-Head Attention (MHA) to obtain useful interactive information. To address another problem, we adopted target-context pair and Context-Target-Interaction (CTI) in our model.The main contributions of this paper are presented as follows:1.We took advantage of MHA and Location-Point-Wise Feed-Forward Networks (LPFFN) to obtain the hidden state interactively in parallel. Besides, we applied pre-trained Bidirectional Encoder Representations from Transformers (BERT) [8] in our model. 2.We used the CTI and target-context pair to help us obtain and fuse useful information. We also verified the effectiveness of these two methods. 3.We experimented on different public authoritative datasets: restaurant reviews and laptop reviews of the SemEval-2014 Task 4 dataset, the ACL(Annual Meeting of the Association for Computational Linguistics) 14 Twitter dataset, SemEval-2015 Task 12 dataset, SemEval-2016 Task 5 dataset. The experimental results showed our model outperformed state-of-the-art methods.

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“…In the second contribution, titled "Sentiment Classification Using Convolutional Neural Networks" [7], Kim and Jeong deal with the problem of textual sentiment classification. They propose a Convolutional Neural Network (CNN) model consisting of an embedding layer, two convolutional layers, a pooling layer, and a fully-connected layer.…”

Section: New Paths In Sentiment Analysis On Social Mediamentioning

confidence: 99%

Sentiment Analysis for Social Media

Iglesias¹,

Moreno

2019

Applied Sciences

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Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.

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Sentiment Classification Using Convolutional Neural Networks

Cited by 140 publications

References 42 publications

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Multiple Interactive Attention Networks for Aspect-Based Sentiment Classification

Sentiment Analysis for Social Media

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