Attention-based LSTM, GRU and CNN for short text classification

Yu, Shujuan; Liu, Danlei; Zhu, Weiguang; Zhang, Yun; Zhao, Shengmei

doi:10.3233/jifs-191171

Cited by 40 publications

(21 citation statements)

References 3 publications

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“…In the subsequent experiments, we use HingeLoss, which has the best overall effect of efficiency and accuracy, as the loss function for labeled data, and RampLoss as the loss function for unlabeled data, as shown in Figure 6(b). Then, in the following chapters [17,18], our optimization objective (12), as shown in Figure 6(d):…”

Section: Application Of Nlp In Text Classificationmentioning

confidence: 99%

Text Classification Based on Machine Learning and Natural Language Processing Algorithms

Li²

2022

Wireless Communications and Mobile Computing

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Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classification method by using machine learning and natural language processing technology. For text classification technology, this paper combines the technical requirements and application scenarios of text classification with ML to optimize the classification. For the application of natural language processing (NLP) technology in text classification, this paper puts forward the Trusted Platform Module (TPM) text classification algorithm. In the experiment of distinguishing spam from legitimate mail by text recognition, all performance indexes of the TPM algorithm are superior to other algorithms, and the accuracy of the TPM algorithm on different datasets is above 95%.

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Section: Application Of Nlp In Text Classificationmentioning

confidence: 99%

Text Classification Based on Machine Learning and Natural Language Processing Algorithms

Li²

2022

Wireless Communications and Mobile Computing

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“…The introduction of transformers‐based embeddings has shown a significant increase in the quality of contextual information extraction 10,87,89 . However, the use of transformers is affected by some limitations, viz., out of vocabulary (OOV) limitation, increased complexity, and importantly the method overfitting in small networks 99,100 . Due to the highlighted drawbacks of transformers, an ensemble of attention and neuro‐fuzzy networks 101 would help reduce the limiting effects, thus increasing classification performance.…”

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

235

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…e GRU model [16], a variant of the recurrent neural network (RNN) gated recurrent unit [17], is particularly suitable for time-series data and also addresses the longtime dependence problem that is important for predictive regression results, coupled with the convolutional neural network (CNN) model that can effectively extract potentially important features between data. e combination of GRU and CNN models [18] is therefore ideally suited to our prediction and regression of corporate financial reports.…”

Section: Building Deep Learning Prediction Models Based On Gru and Cnnmentioning

confidence: 99%

Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry

Cai

2021

Journal of Mathematics

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Recently the issues of insufficient energy and serious air pollution around the world have been rising. Henceforth, there is a need to carry out a research of new energy. Soon, new energy vehicles will be the mainstream trend, which can not only reduce the burden of consumers due to rising fuel prices but also solve the air pollution problem caused by the exhaust emissions of fuel vehicles. With the rapid development of science and technology, deep learning continues to make breakthroughs, and, in the field of economy with huge information data, we have more powerful weapons available to predict and research important economic data with infinite value, which can not only provide reference information to policy makers but also help enterprises and even economic markets to develop more healthily and sustainably. Therefore, this article uses deep learning algorithms to forecast and analyze the new energy industry, starting from the financial information released by new energy vehicle companies in their annual reports, in order to make basic judgments and help policy makers and enterprises in the new energy vehicle industry.

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Attention-based LSTM, GRU and CNN for short text classification

Cited by 40 publications

References 3 publications

Text Classification Based on Machine Learning and Natural Language Processing Algorithms

Text Classification Based on Machine Learning and Natural Language Processing Algorithms

Text‐based emotion detection: Advances, challenges, and opportunities

Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry

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