The tools and technologies required for each case

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depend on the type of analysis to be performed. For example, sentiment analysis is a fuzzy technique that combines natural language processing, computational linguistics, and text analysis to assess an author's attitude toward a certain topic or set of words. Sentiment analysis engines calculate a polarity score, which indicates the degree of positivity or negativity toward a specific topic.

Visualizing and presenting unstructured data is a key aspect of social analytics. Because data is unstructured and qualitative, we cannot rely on quantitative data visualization techniques. Instead, information must be presented in ways that encourage analysis of the information. In other words, we need tools that distill large volumes of data and present it in a way that humans Armenia Mobile Database can easily visualize and understand. Scientific data visualization techniques can be a good option to apply to social analytics visualization, but at the time of writing this article I am not yet aware of any tools that do this effectively. We will undoubtedly begin to see tools that address this need in the near future.



Conclusion
Social analytics is an emerging area that differs substantially from traditional data warehousing and BI. It can be a key differentiator for businesses in the future. However, it should be noted that social analytics represents a significant paradigm shift for IT managers and professionals, requiring new architectures and infrastructure, changing strategies, and new skills. This article will likely raise more questions than it answers. My goal is to make you aware of this trend and invite you to explore these considerations to determine the role that social analytics can play in your organization.