Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

Obayed, Hadab Khalid; Al-Turaihi, Firas Sabah; Alhussayni, Khaldoon H.

doi:10.11591/ijeecs.v23.i1.pp345-353

Cited by 14 publications

(9 citation statements)

References 21 publications

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“…Similar words are clustered together and different words are discarded based on the co-occurrence matrix of a corpus. Rather than training on the entire sparse matrix or individual context windows in a large corpus, the Glove model takes advantage of statistical information as exclusively nonzero elements in a word-word co-occurrence matrix 42 .…”

Section: Methodsmentioning

confidence: 99%

“…TF-IDF quantifies a word in a document by computing the weight of each word which in turn shows the significance of a word in that text 41 . The weight is determined by combining two metrics, TF (Term Frequency) which is a measure of the frequency of a word in a document, and IDF (Inverse Document Frequency) which refers to the measure of the frequency of a word in the entire set of documents.…”

Section: Feature Extractionmentioning

confidence: 99%

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Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

et al. 2022

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Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including ‘not survived’, ‘recovered’, and ‘not recovered’ based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

et al. 2022

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COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people’s perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as ‘recovered’, ‘not recovered’, and ‘survived’. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

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“…Obayes, H.K. et al [21] combined GloVe and bidirectional long short-term memory (BiLSTM) recurrent neural network for better sentiment classification, which causes expensive computation and no guidance for documents containing multiple sentences. Yang, Z. et al [22] proposed Hierarchical attention networks (HAN) for document classification, which maintain a hierarchical structure of word to sentence (building sentence from words) and sentence to document (aggregating sentences to a document representation).…”

Section: Literature Reviewmentioning

confidence: 99%

Corpus Statistics Empowered Document Classification

et al. 2022

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In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of the documents. Gaussian mixture-based clustering is widespread for capturing rich thematic semantics but ignores emphasizing potential terms in the corpus. Moreover, the soft clustering approach causes long-tail noise by putting every word into every cluster, which affects the natural thematic representation of documents and their proper classification. It is more challenging to capture semantic insights when dealing with short-length documents where word co-occurrence information is limited. In this context, for long texts, we proposed Weighted Sparse Document Vector (WSDV), which performs clustering on the weighted data that emphasizes vital terms and moderates the soft clustering by removing outliers from the converged clusters. Besides the removal of outliers, WSDV utilizes corpus statistics in different steps for the vectorial representation of the document. For short texts, we proposed Weighted Compact Document Vector (WCDV), which captures better semantic insights in building document vectors by emphasizing potential terms and capturing uncertainty information while measuring the affinity between distributions of words. Using available corpus statistics, WCDV sufficiently handles the data sparsity of short texts without depending on external knowledge sources. To evaluate the proposed models, we performed a multiclass document classification using standard performance measures (precision, recall, f1-score, and accuracy) on three long- and two short-text benchmark datasets that outperform some state-of-the-art models. The experimental results demonstrate that in the long-text classification, WSDV reached 97.83% accuracy on the AgNews dataset, 86.05% accuracy on the 20Newsgroup dataset, and 98.67% accuracy on the R8 dataset. In the short-text classification, WCDV reached 72.7% accuracy on the SearchSnippets dataset and 89.4% accuracy on the Twitter dataset.

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Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

Cited by 14 publications

References 21 publications

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

Corpus Statistics Empowered Document Classification

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