Comparative analysis of word embedding features to improve the performance of deep learning models on social media data
Year: 2025
Type: article
Abstract: In this study, we apply various deep learning methods incorporating word embedding features to evaluate their impact on improving classification performance in sentiment analysis. The methods employed include conditional random field (CRF), bidirectional long short term memory (BLSTM), and convolutional neural network (CNN). Our experiments utilize social media data from restaurant review. By testing different iterations of these deep learning techniques with various word embedding features, we found that the BLSTM algorithm achieved the highest accuracy of 80.00% before integrating word embedding features. After incorporating word embeddings, the BLSTM with the word2vec feature achieved an accuracy of 87.00%. Notably, the CNN showed a significant improvement with the FastText feature. Considering all evaluation metrics—accuracy, precision, recall, and F1-score—the BLSTM algorithm consistently demonstrated the best performance across different word embeddings.
Authors: Jasmir Jasmir, Pareza Alam Jusia, Yulia Arvita, Gunardi Gunardi
Institution: Universitas Dinamika Bangsa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413002121