社交趋势挖掘:领先还是落后

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hossein Hassani, Nadejda Komendantova, Elena Rovenskaya, Mohammad Reza Yeganegi
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引用次数: 0

摘要

这项研究强调了社会智能挖掘的深刻含义,特别是使用开放获取数据和谷歌搜索引擎数据进行趋势识别。利用先进的分析方法,包括小波相干分析和相位差,揭示了社会数据中隐藏的关系和模式。这些技术提供了对社会现象动态的丰富理解,支持了决策过程。这项研究的多功能性跨越了无数领域,提供了对公众情绪的洞察和对战略方法的预见。研究结果表明,社会智能挖掘在影响不同行业的战略、促进创新和增加价值方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Trend Mining: Lead or Lag
This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. These techniques furnish an enriched comprehension of social phenomena dynamics, bolstering decision-making processes. The study’s versatility extends across myriad domains, offering insights into public sentiment and the foresight for strategic approaches. The findings suggest immense potential in Social Intelligence Mining to influence strategies, foster innovation, and add value across diverse sectors.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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