Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
A Comparative analysis of word embedding and deep learning for Arabic sentiment classification
SF Sabbeh, HA Fasihuddin - Electronics, 2023 - mdpi.com
Sentiment analysis on social media platforms (ie, Twitter or Facebook) has become an
important tool to learn about users' opinions and preferences. However, the accuracy of …
important tool to learn about users' opinions and preferences. However, the accuracy of …
Real-time traffic, accident, and potholes detection by deep learning techniques: a modern approach for traffic management
S Babbar, J Bedi - Neural Computing and Applications, 2023 - Springer
The practical applications of social media have raised the bar for real-time event detection
all over the globe. It has been deemed useful for extracting important data that enables …
all over the globe. It has been deemed useful for extracting important data that enables …
A fistful of vectors: a tool for intrinsic evaluation of word embeddings
The utilization of word embeddings—powerful models computed through Neural Network
architectures that encode words as vectors—has witnessed rapid growth across various …
architectures that encode words as vectors—has witnessed rapid growth across various …
[HTML][HTML] Word embedding and classification methods and their effects on fake news detection
J Hauschild, K Eskridge - Machine Learning with Applications, 2024 - Elsevier
Natural language processing contains multiple methods of translating written text or spoken
words into numerical information called word embeddings. Some of these embedding …
words into numerical information called word embeddings. Some of these embedding …
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
LN Tung, S Cho, X Du, N Neelofar, V Terragni… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine learning (ML) for text classification has been widely used in various domains, such
as toxicity detection, chatbot consulting, and review analysis. These applications can …
as toxicity detection, chatbot consulting, and review analysis. These applications can …
Dynamic Q&A of Clinical Documents with Large Language Models
Electronic health records (EHRs) house crucial patient data in clinical notes. As these notes
grow in volume and complexity, manual extraction becomes challenging. This work …
grow in volume and complexity, manual extraction becomes challenging. This work …
Quantifying the effects of built environment on travel behavior in three Chinese cities during COVID-19
C Cao, F Zhen, X Qi, Y Dong, X Huang - Cities, 2025 - Elsevier
The COVID-19 pandemic has directly impacted human travel behavior. Studies have
revealed the relationship between the built environment and infection risk. The impact of the …
revealed the relationship between the built environment and infection risk. The impact of the …
Fake news detection: deep semantic representation with enhanced feature engineering
Due to the widespread use of social media, people are exposed to fake news and
misinformation. Spreading fake news has adverse effects on both the general public and …
misinformation. Spreading fake news has adverse effects on both the general public and …
Word embeddings-based transfer learning for boosted relational dependency networks
Conventional machine learning methods assume data to be independent and identically
distributed (iid) and ignore the relational structure of the data, which contains crucial …
distributed (iid) and ignore the relational structure of the data, which contains crucial …