A Double Channel CNN-LSTM Model for Text Classification

Liang, Shengbin; Zhu, Bin; Cheng, Suying; Jin, Jiangyong

doi:10.1109/hpcc-smartcity-dss50907.2020.00169

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…Both of them could achieve excellent classification effects [11]. Moreover, the combination of these two techniques has been very successful in the field of natural language processing [12]. We adopted it for the processing of the texts in our corpus.…”

Section: Methodology and Contributionmentioning

confidence: 99%

DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

Bousmaha¹,

Hamadouche²,

Gourara³

et al. 2022

RIA

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Today, many sources of unstructured information such as social networks and blogs are more or less freely available on the web, and their volume is constantly growing, which constitutes a free gold mine for collecting public opinion. Opinion plays a crucial role because it can influence the decision-making process. Sentiment or opinion analysis is a discipline that can be used to meet decision-making needs, provide feedback to new product launches and marketing campaigns, and protect a company’s reputation, especially in social networking environments with massive data by exploiting textual data generated by users. In contrast to the techniques used, we have used and adapted in this study deep learning techniques: CNN and LSTM to identify their potential in this area and apply it to a corpus of Arabic data and in particular in Algerian dialect collected from social networks (50572 Facebook comments). We obtained promising results with an 85% f-measure. This represents a good start for an opinion analysis on the Algerian Dialect.

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Section: Methodology and Contributionmentioning

confidence: 99%

DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

Bousmaha¹,

Hamadouche²,

Gourara³

et al. 2022

RIA

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“…This combined CNN-LSTM model is good at time-based analysis and abstracting meaningful features. Its widespread applications include computer vision and natural language processing with highly satisfactory results (Liang et al 2020). Our crude oil price prediction model learns a function that maps a sequence of past observations, i.e., past oil prices, as input to an output observation, i.e., the future oil price.…”

Section: The Hybrid Model Architecturementioning

confidence: 99%

A Hybrid Deep Learning Approach for Crude Oil Price Prediction

Aldabagh,

Zheng,

Mukkamala

2023

JRFM

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Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions.

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“…ii. Mean Square Error (MSE) determines the squared difference between the forecasted values and observed parameters in terms of the mean [38] as shown in the equation below. The smaller value of MSE of the model, the better performance of the forecasting model.…”

Section: Evaluation Metric Performancementioning

confidence: 99%

“…iii. Mean Absolute Error (MAE) determines the closeness of the forecasted cases of infection to the confirmed cases of COVID-19 [35,38]. The MAE is determined with equation as follows:…”

Section: Evaluation Metric Performancementioning

confidence: 99%

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

et al. 2022

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Background COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors. Methods Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. Results The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Conclusions Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

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A Double Channel CNN-LSTM Model for Text Classification

Cited by 12 publications

References 16 publications

DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

A Hybrid Deep Learning Approach for Crude Oil Price Prediction

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

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