Sentiment trend analysis in social web environments

Kyunglag Kwon, Yunwan Jeon, Chanho Cho, Jongwoo Seo, In-Jeong Chung, Hansaem Park

Abstract: In this paper, we propose a novel method for sentiment trend analysis using Ant Colony Optimization (ACO) algorithm and SentiWordNet. We first collect social data in the form of Resource Description Framework (RDF) triples, and then use ACO algorithm to digitize the amassed RDF triples. Using ACO algorithm, we then compute pheromone values to extract the trends of the user's sentiments with the modified equations. Next, we compute the user's sentiment scores for the computed pheromone values with respect to the sentiment words with SentiWordNet. Finally, we analyze the sentiment trend of the online user by time. For verification of the proposed method, we conduct experiments, and compare the analyzed sentiment trends with their real daily lives. The results show that the proposed method performs satisfactory sentiment trend analysis on real data.