A Bio-inspired Approach to Sentiment Analysis using Ant Colony Optimization - Mishra, Prativa; kumari, Shalini; V, Lavanya (Journal of Neonatal Surgery, 2025)
Sentiment analysis is a crucial natural language processing (NLP) task, particularly for customer reviews. Most of the classical sentiment analysis models are not efficient to handle highdimensional data, and thus computational complexity increases and efficiency decreases. In this paper, a new bio-inspired sentiment analysis approach is proposed by employing Ant Colony Optimization (ACO) for feature selection. Through the imitation of ant colonies' behavior, ACO can effectively discover and pick out the most promising features of text data and improve the accuracy and efficiency of classification models. We have tested the ACO-based solution on Amazon product reviews and applied it to both standard machine learning classifiers (Logistic Regression - 80.5%, SVM - 80.5%, Naive Bayes - 79.5%, Random Forest - 79.0%) and deep learning models (LSTM - 78.5%, BERT - 80.0%). The ACO algorithm was more accurate and less computationally costly in all the models, but particularly the Logistic Regression classifier. The present study validates ACO as a scalable and effective method for real-world application in e- commerce, business intelligence, and social surveillance.