A Deep Learning–based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets

Jamal, Nasir; Chen, Xianqiao; Al‐Turjman, Fadi; Ullah, Farhan

doi:10.1145/3410570

Cited by 13 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Experimental Results and Analysis Of Emotionmentioning

confidence: 99%

“…In order to verify the learning and expression ability of the method in this study, the same features are used to compare the methods in reference [27], reference [28], and the proposed method. e recognition rates of reference [27] and reference [28] are 87.11% and 87.69%, respectively. When the structure of the proposed method is 2000-600-300, the combination of four features achieves the best classification accuracy of 90.94%.…”

Section: Experimental Results and Analysis Of Emotionmentioning

confidence: 99%

See 1 more Smart Citation

Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Jun-ya

2022

Mobile Information Systems

View full text Add to dashboard Cite

Aiming at the problem that the existing methods in the big data environment cannot extract the emotional features of microblog sufficiently and the average accuracy of analysis results is low, a microblog emotion analysis method using deep learning in spark big data environment is proposed. First, the Jieba word segmentation method is used to process text comments, so as to reduce the interference of irregular grammar and nonstandard words on the emotion analysis task of microblog text. Then, features based on affective rules, unary word features, syntactic features, and dependent word collocation features are selected. In order to prevent the dimension disaster caused by excessive feature dimensions, the feature selection method of information gain is used to reduce the dimension of features. Finally, a microblog emotion analysis method based on deep belief network (DBN) is established, and the DBN is parallelized through spark cluster to shorten the training time. Experiments show that when the feature set is composed of TOP2000 features, the classification accuracy of the fusion of four features is 90.94%, which is higher than that of the comparison method. In addition, the training time of DBN algorithm parallelized by spark cluster is only 27.78% of that of single machine. Therefore, compared with the comparison method, the proposed method can significantly improve the performance of the microblog emotion analysis system.

show abstract

Section: Experimental Results and Analysis Of Emotionmentioning

confidence: 99%

Section: Experimental Results and Analysis Of Emotionmentioning

confidence: 99%

Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Jun-ya

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…LSTMs help to classify, analyze and evaluate time series related data. In sentiment classification research [1], stacking multiple LSTM layers is used for sequence classification. However, this paper uses BiLSTM [6] to capture long-range dependencies on feature maps that consist of a forward and a backward LSTM.…”

Section: Sequence Modelingmentioning

confidence: 99%

“…Text is an important tool for computers to recognize and understand the world, and many researchers are engaged in the research of text-related topics, such as text emotion classification [1], text document security [2], and scene text recognition [3], etc. Scene Text Recognition (STR) refers to recognizing text in different natural environments, such as billboards, road signs, trademarks, etc.…”

Section: Introductionmentioning

confidence: 99%

Representative Batch Normalization for Scene Text Recognition

2022

KSII TIIS

View full text Add to dashboard Cite

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP .

show abstract

“…Abuhay et al employ the classic time series forecasting model AutoRegressive Integrated Moving Averages (ARIMAs) to predict the trend of research topics of international conference of computer science 17 . With the development of deep neural networks, 18 long short‐term memory (LSTM) is employed to capture timing sequence features, 19 and used in topic prediction tasks. Using 19,164 publications and 25 journals, Liang et al first predict the future popularity scores of candidate topics in a time series, and then apply LSTM to predict the popularity scores of candidate topics to determine future research topics 20 .…”

Section: Introductionmentioning

confidence: 99%

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Xue

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots.However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.

show abstract

A Deep Learning–based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets

Cited by 13 publications

References 35 publications

Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Representative Batch Normalization for Scene Text Recognition

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Contact Info

Product

Resources

About