Sentiment Analysis of Chinese Microblog Based on Stacked Bidirectional LSTM

Zhou, Junhao; Lu, Yue; Dai, Hong-Ning; Wang, Hao; Hong, Xia

doi:10.1109/access.2019.2905048

Cited by 99 publications

(56 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with traditional ML, deep learning (DL) approaches provide a promising way in capturing the complex features from a huge volume of data so as to have diverse industrial applications, such as sentimental analysis [13] and electricity-theft detection [14]. Furthermore, DL approaches have also been applied in traffic prediction.…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

“…Consequently, the robustness of model can be improved and the computational cost for traffic flow prediction can be reduced. We then combine results Y wide [11][12][13][14]. Finally, We select the root mean square prop (RMSProp) optimizer to minimize the square errors between the prediction value and the actual target value (line 15).…”

Section: Algorithm Analysismentioning

confidence: 99%

See 1 more Smart Citation

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively-available traffic data collected from various sensors in Transportation Cyber-Physical Systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, we propose a wide-attention and deep-composite (WADC) model consisting of a wide-attention module and a deep-composite module in this paper. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with selfattention mechanism. The deep-composite module can generalize local key features via Convolutional Neural Network component and Long Short-Term Memory Network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

show abstract

Section: B Deep Learning Approachesmentioning

confidence: 99%

Section: Algorithm Analysismentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A good text representation is the basis of further mining text information. At present, the mainstream text representation methods include word2vec [21][22][23], FastText [24] and GloVe [25].…”

Section: Text Representation Based On Glovementioning

confidence: 99%

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

et al. 2020

View full text Add to dashboard Cite

In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

show abstract

“…In 2018, Bi-LSTM model has reach state-of-theart for Bengali sentiment analysis, outperformed SVM and Decision Tree [1]. Bi-LSTM with word embedding as input features also showed significant performance on sentiment analysis compare to other several methods such as RNN, CNN, LSTM, Naive Bayes [9,10,12]. The method we proposed for building Indonesian sentiment analysis model is to use paragraph vector as additional input features of Bi-LSTM.…”

Section: Related Workmentioning

confidence: 99%

Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector

Purwarianti

Crisdayanti

2019

2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)

View full text Add to dashboard Cite

Bidirectional Long Short-Term Memory Network (Bi-LSTM) has shown promising performance in sentiment classification task. It processes inputs as sequence of information. Due to this behavior, sentiment predictions by Bi-LSTM were influenced by words sequence and the first or last phrases of the texts tend to have stronger features than other phrases. Meanwhile, in the problem scope of Indonesian sentiment analysis, phrases that express the sentiment of a document might not appear in the first or last part of the document that can lead to incorrect sentiment classification. To this end, we propose the using of an existing document representation method called paragraph vector as additional input features for Bi-LSTM. This vector provides information context of the document for each sequence processing. The paragraph vector is simply concatenated to each word vector of the document. This representation also helps to differentiate ambiguous Indonesian words. Bi-LSTM and paragraph vector were previously used as separate methods. Combining the two methods has shown a significant performance improvement of Indonesian sentiment analysis model. Several case studies on testing data showed that the proposed method can handle the sentiment phrases position problem encountered by Bi-LSTM.

show abstract

Sentiment Analysis of Chinese Microblog Based on Stacked Bidirectional LSTM

Cited by 99 publications

References 21 publications

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector

Contact Info

Product

Resources

About