Sentiment Classification with Convolutional Neural Networks: An Experimental Study on a Large-Scale Chinese Conversation Corpus

Zhang, Lei; Chen, Chengcai

doi:10.1109/cis.2016.0046

Cited by 26 publications

(15 citation statements)

References 2 publications

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“…Artificial intelligence has been applied to deal with various types of medical data 12 . Convolutional neural network (CNN) is one deep learning method which has been proven successful not only on image data 13,14 but in many other fields 15–18 in terms of classification and recognition of images. As the use of CNN increases, there exist many CNN‐based methods whose network architectures comprise more than 10 million parameters to achieve good effectiveness.…”

Section: Methodsmentioning

confidence: 99%

Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

Huang

Hsu

Lee

et al. 2020

The Journal of Dermatology

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Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties for AI in diagnosing skin cancers, including lack of testing for consistency, lack of pathological proof or ambiguous comparisons. Hence, to develop a reliable, feasible and user‐friendly platform to facilitate the automatic diagnostic algorithm is important. The aim of this study was to build a light‐weight skin cancer classification model based on deep learning methods for aiding first‐line medical care. The developed model can be deployed on cloud platforms as well as mobile devices for remote diagnostic applications. We reviewed the medical records and clinical images of patients who received a histological diagnosis of basal cell carcinoma, squamous cell carcinoma, melanoma, seborrheic keratosis and melanocytic nevus in 2006–2017 in the Department of Dermatology in Kaohsiung Chang Gung Memorial Hospital (KCGMH). We used the deep learning models to identify skin cancers and benign skin tumors in the manner of binary classification and multi‐class classification in the KCGMH and HAM10000 datasets to construct a skin cancer classification model. The accuracy reached 89.5% for the binary classifications (benign vs malignant) in the KCGMH dataset; the accuracy was 85.8% in the HAM10000 dataset in seven‐class classification and 72.1% in the KCGMH dataset in five‐class classification. Our results demonstrate that our skin cancer classification model based on deep learning methods is a highly promising aid for the clinical diagnosis and early identification of skin cancers and benign tumors.

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Section: Methodsmentioning

confidence: 99%

Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

Huang

Hsu

Lee

et al. 2020

The Journal of Dermatology

View full text Add to dashboard Cite

show abstract

“…Unlike English text, Chinese text tasks usually require word segmentation because every character in Chinese is connected together [15]. Current Chinese text classification tasks are usually based on a corpus of news, social networks and conversations [6], [22], [23], likely because they are closer to natural language expression, are easier to obtain and have a large research base. However, other areas that have a higher research value and vast market application foreground should not be ignored, for example, the text of radiology records.…”

Section: Chinese Radiology Text a Descriptionmentioning

confidence: 99%

“…The main methods of text classification include traditional methods [1]- [3] and deep learning-based methods [4]- [9]. In recent years, deep learning-based methods, e.g., the convolutional neural network (CNN) and long short-term memory (LSTM) networks, have been widely used in text classification tasks, showing better performance than the traditional methods [6], [7]. For instance, in 2018, [8] combined CNN and LSTM to solve text classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing methods are less efficient and incomplete in extracting this deep information and historical information in very long sequences since these methods are likely to have advantages in only one aspect. Chinese text classification methods in recent years have been based mainly on CNN and LSTM [6], [9], [10] as well as their variants and combinations, and they have achieved good results in the classification tasks of different languages [4], [11], [12]. Notably, although the machine learning algorithm has still been very popular in the last two years [13], some study results such as [39] show that deep neural networks have advantages over some machine learning methods in some classification tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, although the machine learning algorithm has still been very popular in the last two years [13], some study results such as [39] show that deep neural networks have advantages over some machine learning methods in some classification tasks. Although [5] and [6] achieved good results using CNN-based methods, it is difficult for these methods to extract context features in the text due to the CNN structure itself. The method of Zhou et al [15] can collect sequence information; however, it is difficult to obtain some high-weight local features and spatial information in Chinese text just with recurrent neural networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical Comprehensive Context Modeling for Chinese Text Classification

et al. 2019

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The Chinese text classification task is challenging compared to tasks based on other languages such as English due to the characteristics of the Chinese text itself. In recent years, some popular methods based on deep learning have been used for text classification, such as the convolutional neural network (CNN) and the long short-term memory (LSTM) network. However, some problems are still encountered when classifying Chinese text. For example, important but obscure context information in Chinese text is not easily extracted. To improve the effect of Chinese text classification, we propose a novel classification model in this paper named the hierarchical comprehensive context modeling network (HCCMN) that can extract more comprehensive context. Our approach aims to extract contextual information and integrate it with the original input and then extract hierarchically more context, spatial information and high-weight local features from the integrated results. In addition, our method can remember long-term historical obscure information. Since Chinese radiology texts are complicated and difficult to obtain, we collected a Chinese radiology medical text dataset (CIRTEXT) containing more than 56,000 real-world data samples to verify the effect of this work. We conducted experiments on four datasets and showed that our HCCMN performs at state-of-the-art levels on three selected evaluation metrics compared to baselines. We present promising results showing that our hierarchical context modeling network extracts useful context from Chinese text more effectively and comprehensively. INDEX TERMS Chinese text, classification, contextual information, deep neural network.

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Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness

Wei

Feng

Wang

et al. 2019

Natural Language Processing and Chinese Computing

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Sentiment Classification with Convolutional Neural Networks: An Experimental Study on a Large-Scale Chinese Conversation Corpus

Cited by 26 publications

References 2 publications

Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

Hierarchical Comprehensive Context Modeling for Chinese Text Classification

Attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness

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