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

Abstract

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.

A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews

“…Sentiment Analysis with Deep Learning: A Survey by [11] provides an overview of the various deep learning approaches that have been used for sentiment analysis, including CNNs, LSTMs, and BERT. [12] presents a method for sentiment analysis of drug reviews using a combination of a lexicon-based approach and a deep learning model. Twitter Sentiment Analysis Operational Flow [14] The lexicon generation is modular, and the sentiment analysis is performed using a fusion of a convolutional neural network (CNN) and a recurrent neural network (RNN) with threshold weighted mapping.…”

Improving Bert Model Accuracy for Uni-modal Aspect-Based Sentiment Analysis Task

“…Opinion Mining (OM) and Sentiment Analysis have emerged as established research fields over the last twenty years, finding widespread use in various applications, including commercial ones. The definitions of OM and SA and any distinctions between opinion, sentiment, emotion, affect, and related concepts continue to be unclear despite the enormous advancement and innovation in these disciplines [7]. Many academics contend that these notions are separate and call for various methodologies that result in multiple conclusions, even though some see this as a dispute over language with no genuine difference between these concepts.…”