Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

Sweidan, Asmaa Hashem; El-Bendary, Nashwa; Al-Feel, Haytham

doi:10.1109/access.2021.3091394

Cited by 39 publications

(9 citation statements)

References 45 publications

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“…The proposed MLBTWCR is executed on the http://druglib.com and http://drugs.com datasets and it is compared with the existing methods such as 3W3DT, 21 ABCDM, 28 and COXL‐net 29 for metrics like recall, accuracy, error rate, FPR, F‐measure, and FNR.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN‐RNN

Dubey¹,

Singh

Sheoran³

et al. 2022

Concurrency and Computation

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Summary In drug review sentimental analysis (SA), users can share their experiences after consuming the drugs, which provides an accurate decision about the safety of the drug and public health. Patient‐written medical and health‐care reviews are among the most valuable and informative textual content on social media, but researchers in the areas of natural language processing (NLP) and data mining have not researched them thoroughly. These reviews provide insight into patients' interactions with doctors, treatment, and satisfaction or dissatisfaction with health services. The existing approaches have some problems like exploding/vanishing gradients and do not have sequential modeling. When learning long reviews, the exploding and vanishing gradient problems occurs. This problem makes it hard to tune parameters and learn in the network. The existing methods do not have sequential modeling because they fail to extract long dependencies for long reviews in both backward and forward directions. To overcome these issues, we proposed a Modular Lexicon Generation and a Fusion of Bidirectional threshold weighted mapping CNN‐RNN (MLBTWCR) for classifying drug reviews based on users opinions. The Aspect based Modular Lexicon generation using the Advanced Dragon Fly Algorithm (AMLDA) is used to generate the score values for the lexicon and labels based on aspect. The Bidirectional Dropout Long and Short‐Term Memory (Bi‐DLSTM) and Bidirectional Gated Recurrent Unit (Bi‐GRU) used for extracting long dependencies and for performing the sequence of arbitrary length in both backward and forward directions. The experimental results are evaluated using http://drugslib.com and http://drugs.com datasets. Based on evaluation result, the proposed MLBTWCR gives accuracy of 93.02%, recall of 88.72%, error rate of 11.2, false positive rate (FPR) of 11.3, false negative rate (FNR) of 13.6, running time of 15 s, and convergence speed of 0.2 and F‐measure of 92.64%. Hence, our method performs well for the drug reviews classification based on aspects.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…However, it is incapable of performing aspect‐level SA. Sweidan et al 29 offered combined ontology XLNet (COXL‐net) learning for the aspect level SA. This method improves the feature extraction quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN‐RNN

Dubey¹,

Singh

Sheoran³

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Bayes (Goel et al 2016), LSTM (Huang et al 2021), BI-LSTM (Lin et al 2021), XLNet (Sweidan et al 2021), BERT , RoBERTa (Liao et al 2021), ULMFiT (Kulkarni et al 2021), ELMo (Nurifan et al 2019), and Albert (Ding et al 2021) to classif y text polarity. The methods perform well and are also reliable (Bhargava & Rao, 2018;Singh & Goel, 2019).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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“…Alshahrani [12] identified the optimism and pessimism in Twitter messages through finetune emotion analysis XLNet model, and achieved significant results. Sweidan [13] used XLNet in combination with the BiLSTM network for the aspect emotion classification in online user reviews, improving the overall accuracy of emotion classification. Use XLNet dynamic text representation instead of traditional static text representation to perform the initialization of downstream tasks.…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Jiang¹,

Wang²,

Jiang³

et al. 2023

JCC

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Emotion has a nearly decisive role in behavior, which will directly affect netizens' views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend.

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Sentence-Level Aspect-Based Sentiment Analysis for Classifying Adverse Drug Reactions (ADRs) Using Hybrid Ontology-XLNet Transfer Learning

Cited by 39 publications

References 45 publications

Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN‐RNN

Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN‐RNN

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

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