期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 33, 期 3, 页码 523-547出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-012-0512-y
关键词
Social network; Blog; Collective behavior; Sentiment analysis; Ant colony; Swarm intelligence; Supervised learning; Trend prediction
资金
- Birla Institute of Technology, International Center Mauritius
- US National Science Foundation within the Directorate for Computer & Information Science & Engineering's (CISE) Division of Information & Intelligent Systems (IIS) [IIS-1110868, IIS-1110649]
- US Office of Naval Research [N000141010091]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1110868] Funding Source: National Science Foundation
With the rapid growth of the availability and popularity of interpersonal and behavior-rich resources such as blogs and other social media avenues, emerging opportunities and challenges arise as people now can, and do, actively use computational intelligence to seek out and understand the opinions of others. The study of collective behavior of individuals has implications to business intelligence, predictive analytics, customer relationship management, and examining online collective action as manifested by various flash mobs, the Arab Spring (2011) and other such events. In this article, we introduce a nature-inspired theory to model collective behavior from the observed data on blogs using swarm intelligence, where the goal is to accurately model and predict the future behavior of a large population after observing their interactions during a training phase. Specifically, an ant colony optimization model is trained with behavioral trend from the blog data and is tested over real-world blogs. Promising results were obtained in trend prediction using ant colony based pheromone classier and CHI statistical measure. We provide empirical guidelines for selecting suitable parameters for the model, conclude with interesting observations, and envision future research directions.
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