Comparing deep learning architectures for sentiment analysis on drug reviews

Colón-Ruíz, Cristobal; Segura-Bedmar, Isabel

doi:10.1016/j.jbi.2020.103539

Cited by 127 publications

(54 citation statements)

References 7 publications

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“…[19]. A sentiment rating on the other hand, refers to the use of numerical values (or stars) to indicate the intensity of one's sentiment [10]. As mentioned previously, sentiment analysis has been studied and applied in various fields primarily to gauge how people feel about something, and its popularity among research scholars can be attributed to the proliferation of social media.…”

Section: Sentiment Analysis and Customer Reviewsmentioning

confidence: 99%

“…Sentiment analysis generally elicits a sentiment orientation (i.e., positive, neutral, negative) of textual information, which can improve decision-making processes for multitude domains including businesses such as finance and stock market [5][6][7], digital payment services [4], retails [2,8], and products [1,3,9], among others. Scholars investigating sentiment analysis based on textual communications have also examined or attempted to determine the sentiment ratings, often using scales ranging from 1 to 5 or 10 (i.e., higher scores indicate more positive reviews) [10]. Though often performed using machine learning approaches, deep learning has gained momentum in sentiment analysis in recent years showing promising results [6,10].…”

Section: Introductionmentioning

confidence: 99%

“…Scholars investigating sentiment analysis based on textual communications have also examined or attempted to determine the sentiment ratings, often using scales ranging from 1 to 5 or 10 (i.e., higher scores indicate more positive reviews) [10]. Though often performed using machine learning approaches, deep learning has gained momentum in sentiment analysis in recent years showing promising results [6,10]. Further, scholars have also explored various word embedding techniques including the popular Word2Vec and its variants to the more advanced and state-of-art transformer-based pre-trained models such as Bi-directional Encoder Representations from Transformers (BERT) [10][11][12][13] that have displayed much better results in text classifications.…”

Section: Introductionmentioning

confidence: 99%

“…Though often performed using machine learning approaches, deep learning has gained momentum in sentiment analysis in recent years showing promising results [6,10]. Further, scholars have also explored various word embedding techniques including the popular Word2Vec and its variants to the more advanced and state-of-art transformer-based pre-trained models such as Bi-directional Encoder Representations from Transformers (BERT) [10][11][12][13] that have displayed much better results in text classifications. Nevertheless, as shown later in Sect.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, as shown later in Sect. 2.2, studies exploring deep learning algorithms, particularly those exploring and comparing various embedding techniques are lacking, both for English and non-English datasets [10,12,13]. Moreover, recent reviews show studies exploring data augmentation techniques in supervised deep learning algorithms to improve prediction improvements [14].…”

Section: Introductionmentioning

confidence: 99%

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SM‐Detector: A security model based on BERT to detect SMiShing messages in mobile environments

Ghourabi

2021

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The growing use of SMS by businesses to communicate with their customers has made attackers more interested in smishing attacks. Smishing is a security attack that involves sending a fake SMS message in order to steal the personal credentials of mobile users. This kind of attack has become a serious cyber-security issue and has caused great financial losses for both people and businesses. In this article we propose a hybrid security model called "SM-Detector" aiming to detect smishing messages in mobile environments. To increase the efficiency of "SM-Detector," we have combined three different detection methods: (i) identification of malicious URLs, (ii) identification of suspected words, phone numbers and emails with regular expression analysis, and (iii) classification of messages using BERT-based algorithms to distinguish spam messages. "SM-Detector" also includes a mobile application allowing the user to check their SMS and report smishing messages. Its strength is that it can deal with mixed text messages written in Arabic or English. The experimental evaluation conducted on English and Arabic datasets showed a remarkable accuracy of 99.63%.

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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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Comparing deep learning architectures for sentiment analysis on drug reviews

Cited by 127 publications

References 7 publications

A deep learning approach in predicting products’ sentiment ratings: a comparative analysis

A deep learning approach in predicting products’ sentiment ratings: a comparative analysis

SM‐Detector: A security model based on BERT to detect SMiShing messages in mobile environments

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

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