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Jurnal Institusi
The Impact of Feature Extraction to Naïve Bayes Based Sentiment Analysis on Review Dataset of Indihome Services
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi; Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi ; 1-10
Indihome is a product of PT Telekomunikasi Indonesia as an internet service provider or internet service provider (ISP) in Indonesia. Every product or service offered to the public certainly has its advantages and disadvantages, as well as ...
Jurnal Institusi
LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi; Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi ; 79-89
The implementation of the Covid-19 vaccination carried out by Indonesian government was ignited pros and contras among the public. Certainly, there will be pros and cons about the vacci...
Jurnal Institusi
IMPLEMENTASI BI-DIRECTIONAL LONG SHORT TERM MEMORY TERHADAP KLASIFIKASI SENTIMEN DI TWITTER PADA DATASET TERBATAS
ZONAsi: Jurnal Sistem Informasi; Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025; 11 - 25
Perkembangan teknologi informasi telah mengubah cara Masyarakat mengekspresikan pendapat, terutama melalui media sosial seperti Twitter. Di bidang politik, media sosial kerap dijadikan parameter untuk mengukur popularitas tokoh politik sampai kepada ...
PubMed
The Spectral Underpinning of word2vec
Front Appl Math Stat
Word2vec introduced by Mikolov et al. is a word embedding method that is widely used in natural language processing. Despite its success and frequent use, a strong theoretical justification is still lacking. The main contribution of our paper is to p...
PubMed
Optimizing word embeddings for small datasets: a case study on patient portal messages from breast cancer patients
Sci Rep
Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processin...
PubMed
Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples
Res Sq
BACKGROUND: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural langua...
PubMed
Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts
SN Comput Sci
Analyze performance of unsupervised embedding algorithms in sentiment analysis of knowledge-rich data sets. We apply state-of-the-art embedding algorithms Word2Vec and Doc2Vec as the learning techniques. The algorithms build word and document embeddi...
PubMed
Word2vec convolutional neural networks for classification of news articles and tweets
PLoS One
Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning. T...
PubMed
Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution
PeerJ Comput Sci
BACKGROUND: Topic modeling approaches, such as latent Dirichlet allocation (LDA) and its successor, the dynamic topic model (DTM), are widely used to identify specific topics by extracting words with similar frequencies from documents. However, these...
PubMed
LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things
Sensors (Basel)
Log anomaly detection is an efficient method to manage modern large-scale Internet of Things (IoT) systems. More and more works start to apply natural language processing (NLP) methods, and in particular word2vec, in the log feature extraction. Word2...
PubMed
Word Embeddings as Statistical Estimators
Sankhya Ser B
Word embeddings are a fundamental tool in natural language processing. Currently, word embedding methods are evaluated on the basis of empirical performance on benchmark data sets, and there is a lack of rigorous understanding of their theoretical pr...
PubMed
Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke: A Feasibility Study
Int J Environ Res Public Health
Background: The possible benefits of using semantic language models in the early diagnosis of major ischemic stroke (MIS) based on artificial intelligence (AI) are still underestimated. The present study strives to assay the feasibility of the word2v...
PubMed
Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data
Heliyon
The process data in computer-based problem-solving evaluation is rich in valuable implicit information. However, its diverse and irregular structure poses challenges for effective feature extraction, leading to varying degrees of information loss in ...
PubMed
PTPD: predicting therapeutic peptides by deep learning and word2vec
BMC Bioinformatics
*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational mo...
PubMed
Comparison of the accuracy of Japanese synonym identifications using word embeddings in the radiological technology field
Sci Rep
The terminology in radiological technology is crucial, encompassing a broad range of principles from radiation to medical imaging, and involving various specialists. This study aimed to evaluate the accuracy of automatic synonym detection considering...
PubMed
AttnW2V-Enhancer: Leveraging attention and Word2Vec for enhanced enhancer prediction
Comput Struct Biotechnol J
Accurate identification of enhancer regions in DNA sequences is essential for understanding gene regulation and its role in diverse biological processes. Enhancers are regulatory elements that influence gene expression, but their detection remains ch...
PubMed
iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec
Front Genet
Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer’s disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel–drug, comput...
PubMed
Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks
PLoS One
Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been ...
PubMed
Refining Semantic Similarity of Paraphasias Using a Contextual Language Model
J Speech Lang Hear Res
PURPOSE: ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia's naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as wel...
PubMed
Sentiment analysis using long short term memory and amended dwarf mongoose optimization algorithm
Sci Rep
The use of machine learning to analyze sentiments has attained considerable interest in the past few years. The task of analyzing sentiments has becfigome increasingly important and challenging. Due to the specific attributes of this type of data, in...