Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Abdelkarim Ait Lahcen
Ibn Tofail University
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Effect of word embedding vector dimensionality on sentiment analysis through short and long texts Mohamed Chiny; Marouane Chihab; Abdelkarim Ait Lahcen; Omar Bencharef; Younes Chihab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp823-830

Abstract

Word embedding has become the most popular method of lexical description in a given context in the natural language processing domain, especially through the word to vector (Word2Vec) and global vectors (GloVe) implementations. Since GloVe is a pre-trained model that provides access to word mapping vectors on many dimensionalities, a large number of applications rely on its prowess, especially in the field of sentiment analysis. However, in the literature, we found that in many cases, GloVe is implemented with arbitrary dimensionalities (often 300d) regardless of the length of the text to be analyzed. In this work, we conducted a study that identifies the effect of the dimensionality of word embedding mapping vectors on short and long texts in a sentiment analysis context. The results suggest that as the dimensionality of the vectors increases, the performance metrics of the model also increase for long texts. In contrast, for short texts, we recorded a threshold at which dimensionality does not matter.
Co-Authors Marouane Chihab Mohamed Chiny Omar Bencharef Younes Chihab