LSTM-CNN Hybrid Model for Text Classification

Zhang, Jiarui; Li, Yingxiang; Tian, Jie; Li, Tongyan

doi:10.1109/iaeac.2018.8577620

Cited by 78 publications

(42 citation statements)

References 6 publications

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“…Then it builds a new CNN model on the LSTM to extract the features of the input text sentences and improve the results of the classification accuracy. In our experiment, we follow the Hybrid framework for Text modeling using LSTM-CNN method applied in previous works [45][46][47]. Figure 2 shows the proposed LSTM-CNN combined model architecture for classifying the sentences with suicidal and non-suicidal content.…”

Section: Proposed Network Modelmentioning

confidence: 99%

“…In each cell, four independent calculations were performed using four gates. The LSTM layer structure with input sequences X = (x t ) with a d-dimensional word embedding vector, while H represents the number of LSTM hidden layer nodes [46].…”

Section: Dropout Layermentioning

confidence: 99%

“…CNN is similar to Feed-forward Neural Networks where the connections between the nodes do not form a cycle. Thus, a single neuron in CNN represents a region within an input sample such as a piece of image or text, in our convolution layer we follow the work by [46].…”

Section: Convolutional Layermentioning

confidence: 99%

“…In our experiment, we use multiple convolutional filters with various parameter initializations to extract multiple maps from the text [46].…”

Section: Convolutional Layermentioning

confidence: 99%

See 3 more Smart Citations

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

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Section: Proposed Network Modelmentioning

confidence: 99%

Section: Dropout Layermentioning

confidence: 99%

Section: Convolutional Layermentioning

confidence: 99%

“…In our experiment, we use multiple convolutional filters with various parameter initializations to extract multiple maps from the text [46].…”

Section: Convolutional Layermentioning

confidence: 99%

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Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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“…Through statistical analysis, the four performance indexes of Accuracy, Recall, Precision, and F1-Score (as shown in FIGURE 4) in the model used in the experiment are higher than they in the CNN-Convolutional Neural Networks model [21]. Through the experimental analysis, based on the above comparison, the improved emotion analysis model based on the Bi-LSTM model has a better effect on the emotion analysis of danmaku data in sports events.…”

Section: B Comparative Experiments With Cnn and Its Visualizationmentioning

confidence: 91%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

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Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Sharbatian,

Moattar

2023

Applied AI Letters

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Users of e‐commerce websites review different aspects of a product in the comment section. In this research, an approach is proposed for opinion aspect extraction and recognition in selling systems. We have used the users' opinions from the Digikala website (www.Digikala.com), which is an Iranian e‐commerce company. In this research, a language‐independent framework is proposed that is adjustable to other languages. In this regard, after necessary text processing and preparation steps, the existence of an aspect in an opinion is determined using deep learning algorithms. The proposed model combines Convolutional Neural Network (CNN) and long‐short‐term memory (LSTM) deep learning approaches. CNN is one of the best algorithms for extracting latent features from data. On the other hand, LSTM can detect latent temporal relationships among different words in a text due to its memory ability and attention model. The approach is evaluated on six classes of opinion aspects. Based on the experiments, the proposed model's accuracy, precision, and recall are 70%, 60%, and 85%, respectively. The proposed model was compared in terms of the above criteria with CNN, Naive Bayes, and SVM algorithms and showed satisfying performance.

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LSTM-CNN Hybrid Model for Text Classification

Cited by 78 publications

References 6 publications

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

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