Comparative analysis of machine and deep learning models with text embeddings for sentiment analysis

2026-01-15T07:45:29-06:00

This study presents a comprehensive comparative evaluation of traditional machine learning (ML) algorithms Naïve Bayes, Random Forest, and Support Vector Machine (SVM) against a deep learning model, Long Short-Term Memory (LSTM), using three distinct text embedding techniques: Term Frequency-Inverse Document Frequency (TF-IDF), FastText, and Word2Vec. A dataset comprising 30,001 social media posts was employed to assess performance across multiple evaluation metrics, including accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Experimental findings reveal that the combination of LSTM with Word2Vec embeddings achieves superior performance, recording an accuracy of 92.65%, an F1-score of 94.37%, a ROC-AUC of 95.70%, and the lowest log loss value of 0.2074. Among the classical machine learning models, Random Forest emerged as the most effective, outperforming Naïve Bayes and SVM in terms of balanced accuracy and generalization capability. The results underscore the pivotal influence of embedding representation in sentiment analysis and demonstrate that deep learning models, when integrated with semantically rich embeddings, can effectively capture contextual dependencies within textual data. The study thus provides valuable insights into developing robust sentiment analysis frameworks and recommends future exploration of hybrid and ensemble learning approaches to enhance generalization and interpretability in real-world natural language processing applications.

The role of artificial intelligence in developing engineering project management

2026-02-02T11:36:20-06:00

This study aims to verify the role of artificial intelligence in developing engineering project management. It seeks to determine the feasibility of implementing artificial intelligence to increase productivity, improve safety, reduce costs, and save time within engineering projects. The focus of the study is on two approaches to project management: traditional, which does not involve extensive integration of the latest intelligent systems, and innovative, where artificial intelligence is used for automated decision-making and risk forecasting. The methodological tools include pairwise comparison methods based on the Saati scale, as well as formulas for integral assessment, which compare the total benefits and opportunities with costs and risks. The results indicate that the innovative approach with deep integration of artificial intelligence has a higher overall indicator due to better productivity, more efficient resource allocation, and a more flexible security system, despite the additional initial costs and risks of implementation. In the long term, this approach allows for significant time savings and improved economic performance, which is critically important in the context of global competition and rapid technological change. This study confirms the feasibility of using artificial intelligence and provides an analytical tool for rational decision-making between traditional and innovative approaches to engineering project management.

Review of Computer Engineering Research

Comparative analysis of machine and deep learning models with text embeddings for sentiment analysis

The role of artificial intelligence in developing engineering project management