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Example of an analytic dashboard on Facebook that looks at the reach of a post. Facebook has their own analytic platform which allows the user to see how other users are interacting with their posts, with the use of multiple metrics. [2] This is not something the average user uses, but rather a tool that is used by pages or public figures, for ...
The product of ad hoc analysis is typically a report or data summary. A deep analysis implies an analysis that spans a long time and involves a large amount of data, which typically translates into a high CPU requirement. [2] Domain of Analysis: The domain of the analysis is broadly classified into external social media and internal social media.
Graph-tool is a python module for efficient analysis of graphs. Its core data structures and algorithms are implemented in C++, with heavy use of Template metaprogramming, based on the Boost Graph Library. It contains a comprehensive list of algorithms. R: Social network analysis within the versatile and popular R environment
Social data analysis usually comprises two key steps: 1) gathering data generated from social networking sites (or through social applications), and 2) analysis of that data, in many cases requiring real-time (or near real-time) data analysis, measurements which understand and appropriately weigh factors such as influence, reach, and relevancy ...
Facebook's Graph API is one such API that social media monitoring solution products would connect to pull data from. [19] Some social media monitoring and analytics companies use calls to data providers each time an end-user develops a query. Others will also store and index social posts to offer historical data to their customers.
Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites. [17] Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web. [74]
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Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.