Results 101 to 110 of about 10,729 (237)

Word Embedding Features to Improve Machine Learning Performance in Sentiment Analysis of the Honor of Kings Game

Sistemasi: Jurnal Sistem Informasi
The rapid growth of social media has encouraged an increasing number of studies on sentiment analysis to better understand public perceptions and opinions.
Abdul Harris, Agus Nugroho, Yudi Novianto, Jasmir Jasmir, Dhea Fatma   +4 more
doaj   +1 more source

Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages

Information, 2019
To overcome the data sparseness in word embedding trained in low-resource languages, we propose a punctuation and parallel corpus based word embedding model.
Yang Yuan, Xiao Li, Ya-Ting Yang
doaj   +1 more source

Algorithm Design for Stock Price Prediction System Based on Distilbert Financial News Sentiment Analysis Model

Engineering Reports, Volume 8, Issue 6, June 2026.
This study develops a lightweight pre‐trained model for financial news sentiment analysis and stock price prediction. By integrating domain adaptation and multimodal fusion, it achieves real‐time efficiency without compromising accuracy, offering a practical solution for intelligent investment and quantitative trading systems. ABSTRACT As the frequency
Jingyu Zhang
wiley   +1 more source

NCHLT isiZulu word2vec-Skipgram embeddings

, 2023
Static word embeddings for the Skipgram flavour of the word2vec (w2v) architecture (Mikolov et al., 2013).
Roald Eiselen
core  

Effect of Dimension Size and Window Size on Word Embedding in Classification Tasks

Acta Informatica Pragensia
Background: Static word embedding models such as Word2Vec and GloVe remain widely used in natural language processing, yet key hyperparameters are often selected heuristically rather than through systematic validation.Objective: This study provides an ...
Dávid Držík, Jozef Kapusta
doaj   +1 more source

Adversarial Training of Word2Vec for Basket Completion

CoRR, 2018
In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user ...
Ugo Tanielian, Mike Gartrell, Flavian Vasile   +2 more
openaire   +2 more sources

A Comprehensive Study of Bengali Language Tools and Applications for Artificial Intelligence Research

Engineering Reports, Volume 8, Issue 6, June 2026.
This study presents a comprehensive analysis of Bengali AI resources, including NLP tools, machine translation systems, speech technologies, and benchmarking datasets. The findings highlight fragmented development across tasks and emphasize the need for integrated resources, unified benchmarks, and multimodal datasets to build a scalable and robust ...
Fairooz Maliha, Supprio Ghosh Nil, Saymadeen Tabassum, Md. Ataur Rahman, Arifa Rahman, Md. Tarek Habib   +5 more
wiley   +1 more source

word2vec Parameter Learning Explained

, 2016
The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been proven to be able to carry semantic meanings and are useful in ...
Xin Rong
core  

NCHLT Xitsonga word2vec-Skipgram embeddings

, 2023
Static word embeddings for the Skipgram flavour of the word2vec (w2v) architecture (Mikolov et al., 2013).
Roald Eiselen
core  

Mental Health Risk Detection From Social Media Text Data: A Scoping Review of the Machine Learning Research Landscape

PsyCh Journal, Volume 15, Issue 3, June 2026.
ABSTRACT Machine learning approaches have been increasingly applied to social media text data for mental health risk detection. However, existing studies vary widely in target outcomes, data sources, labeling strategies, and evaluation practices, and a structured overview of recent research remains limited.
Yiqing He, Yinning He, Darong Liu
wiley   +1 more source