After gathering the data from the social network, the data are preprocessed to execute the processes, like prediction or analysis. Based on the application, the collected data are processed with the preprocessing stages, and the data can be categorized and visualized. Nowadays, in Python, the classifiers implemented for an application is mainly any kind of the machine learning classifier that acts as a supervised machine learning approach. The classifier requires proper training using the labeled training data, without which the performance of the classifier cannot be analyzed. One of the commonly used statistical classifier is the Naïve Bayes classifier, which is generally used to classify the sentiments of people in COVID pandemic conditions. Such kind of classifiers generally utilizes the publicly available data (from the communal media data) in an efficient way to perform a prediction or analysis or classification problems.
Figure 1.3 Flowchart of social network.
1.5 Clarity Toward the Indices Employed in the Social Network Analysis
There are a number of metrics available for the SN analysis methods that measure the activity of the social users/nodes and ensure a better understanding of the analysis [32, 33]. Some of the metrics are discussed as follows:
1.5.1 Centrality
The evaluation of the constructional significance of a node present in an organization is executed using the metric called centrality. In other words, the preeminence of a node in an organization is deliberated using the centrality metrics. The highly influential people in the online social network can be identified using these metrics. A number of measures are used when evaluating the centrality metrics, such as Degree Centrality, Eigenvector Centrality, PageRank measure, Between-ness Centrality, Closeness Centrality, and finally, the group Centrality [34].
1.5.2 Transitivity and Reciprocity
The linking characteristics of a network can be accessed using the transitivity and reciprocity metrics. The transitive nature between three edges can be analyzed using the transitivity metric in such a way to develop a triangle, and in the same way, the transitive nature of a node is analyzed using the reciprocity metrics.
1.5.3 Balance and Status
The consistency of the networks can be evaluated using the social balance and social status metrics. The social balance theory states that a friend relationship is consistent with the propagation of the transitivity among nodes as “the friend of my friend is my friend.” Hence, the consistent triangles, depending on this strategy, are represented as balanced.
1.6 Conclusion
SN organization examination is the way toward researching social designs using organizations and chart hypothesis. It consolidates the assortment of strategies for examining the construction of interpersonal organizations just as speculations that target clarifying the hidden elements; furthermore, designs are seen in these constructions. It is an intrinsically interdisciplinary field, which initially rose up out of the fields of social brain research, insights, and chart hypothesis.
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