Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks

• 作者: Wei Hu William Deitrick
• 来源期刊: 数据分析和信息处理(英文)
• 学科: 工学
• 关键词: COMMUNITY Detection SENTIMENT ANALYSIS TWITTER Online Social NETWORKS MODULARITY Community-Level SENTIMENT ANALYSIS
• 年，卷（期）: 2013,（3）
• 页码范围: 19-29
• 页数: 11页
• 分类号: TP39

摘要

The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.