The BeeSentiment Algorithm (BSA): A Swarm Intelligence Approach to Scalable Online Review Collection and Sentiment Analysis
Year: 2025
Type: article
Abstract: The proliferation of online reviews presents a rich, yet often unstructured, data source for market research, product development, and public sentiment tracking. This paper introduces the BeeSentiment Algorithm (BSA), a novel swarm intelligence approach combining principles of Artificial Bee Colony (ABC) optimisation with advanced AI sentiment analysis for efficient and scalable online review collection and processing. BSA leverages a distributed architecture wherein ‘scout’ bees autonomously identify and evaluate potential review sources based on quantifiable metrics such as review quantity and freshness. Subsequently, ‘onlooker’ bees (employed bees) are probabilistically assigned to optimal sources, extracting review content and performing an initial, lightweight sentiment assessment. Collected reviews are then centrally processed by ‘worker’ bees for de-duplication, robust storage, and in-depth, multi-faceted sentiment analysis, including aspect-based sentiment. This methodology addresses challenges associated with large-scale, dynamic web data extraction and offers granular, real-time insights into consumer sentiment.
Author: Antony Lees
Institution: BPP University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4417437355