Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification

Li, Wenkuan; Li, Dongyuan; Yin, Hongpeng; Zhang, Lindong; Zhu, Zhenfang; Liu, Peiyu

doi:10.3390/app9183717

Cited by 10 publications

(8 citation statements)

References 36 publications

(61 reference statements)

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“…In [16], they propose a lexicon-enhanced attention network (LAN) based on text representation to improve the classification of sentiments. Combining the sentiment lexicon with attention mechanism in the word embedding module, they can obtain the sentiment-aware word embeddings as the input of the deep neural network, which bridges the gap between sentiment linguistic knowledge and deep learning methods.…”

Section: Related Work 21 Sentiment Analysis In Social Mediamentioning

confidence: 99%

Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Nemes

Kiss

2021

Applied Sciences

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Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people’s emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.

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Section: Related Work 21 Sentiment Analysis In Social Mediamentioning

confidence: 99%

Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Nemes

Kiss

2021

Applied Sciences

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“…Liu et al 19 proposed a co-attention mechanism to capture the semantic correlations and generate more excellent feature representation. Li et al 20 proposed a lexicon-enhanced network by combing the sentiment lexicon and attention mechanism and incorporating sentiment linguistic knowledge into the deep learning methods. Meb et al 21 proposed an attention-based model named ABCDM (An attention-based bidirectional CNN-RNN deep model) considering features with different importance, which extract temporal information by utilizing long short term memory (LSTM) and gated recurrent unit layers.…”

Section: Attention Mechanismmentioning

confidence: 99%

Implicit sentiment analysis based on graph attention neural network

Yang

Xing

et al. 2021

Engineering Reports

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Sentiment analysis is one of the crucial tasks in the field of natural language processing. Implicit sentiment suffers a significant challenge because the sentence does not include explicit emotional words and emotional expression is vague. This paper proposed a novel implicit sentiment analysis model based on graph attention convolutional neural network. A graph convolutional neural network is used to propagate semantic information. The attention mechanism is employed to compute the contribution to the emotional expression of words.In order to solve the problem of multiple attention preserving repeated information, orthogonal attention constraint was used to make different attention store different emotional information; given the uneven distribution of emotional information, score attention constraint was proposed to make the model focus on a limited number of essential words. The performance of the proposed model was verified on implicit sentiment datasets. The F value reached 88.16%, which is higher than the benchmark model in the literature. The attention mechanism is analyzed to verify the effectiveness of orthogonal constraint and score constraint.

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“…In the field of NLP, most of the mainstream text classification algorithms are based on convolutional neural network (CNN) and recurrent neural network (RNN) [26][27][28][29]. As a variant of RNN, long short-term memory (LSTM) solves the problem of the long-term dependence of the RNN model and the problem of gradient disappearance and explosion caused by a too long sequence.…”

Section: Network Architecture Of Lstmmentioning

confidence: 99%

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

et al. 2020

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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.

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Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification

Cited by 10 publications

References 36 publications

Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Implicit sentiment analysis based on graph attention neural network

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

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