ArticleThe Journal of supercomputing2022
A deep learning approach in predicting products' sentiment ratings: a comparative analysis.
Article in The Journal of supercomputing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Article
- Predicting opinion using deep learning: From burning to sustainable management of organic waste in Indian State of Punjab.Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2024Article
- A monthly temperature prediction based on the CEEMDAN-BO-BiLSTM coupled model.Scientific reports · 2024Article
- Guide for the application of the data augmentation approach on sets of texts in Spanish for sentiment and emotion analysis.PloS one · 2024Article
- Deep learning-based method for sentiment analysis for patients' drug reviews.PeerJ. Computer science · 2024Article
- Deep neural architecture for natural language image synthesis for Tamil text using BASEGAN and hybrid super resolution GAN (HSRGAN).Scientific reports · 2023Article
- Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models.Heliyon · 2023Article
- Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM.Expert systems with applications · 2023Article
- Topic sentiment analysis based on deep neural network using document embedding technique.The Journal of supercomputing · 2023Article
- A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a benchmark comparison of several deep learning models including Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short Term Memory, assessed based on various word embedding approaches, including the Bi-directional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was administered using the Easy Data Augmentation approach resulting in two datasets (original versus augmented). All the models were assessed in two setups, namely 5-class versus 3-class (i.e., compressed version). Findings show the best prediction models were Neural Network-based using Word2Vec, with CNN-RNN-Bi-LSTM producing the highest accuracy (96%) and
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.