Design and Implementation of Social Media Sentiment and Engagement Pattern using VADER and TextBlob
Keywords:
Engagement analysis, K-Means, Machine learning, Network analysis, Principal component analysis (PCA), Sentiment analysis, Social media analyticsAbstract
The rapid growth of social media and related platforms generates vast amounts of user-created content. This extensive information contains significant material that is shared on social media for specific purposes, and in response, the public expresses their opinions through likes, dislikes, shares, comments, and various reactions to these posts. This study aims to investigate the sentiment and engagement aspects associated with this information, as well as the genuine opinions of the public regarding these posts. It is a challenging task due to the unstructured nature of the content, the high dimensionality of public inputs, and the complexity of the information. Traditional analytical methods and two-dimensional visualization techniques often struggle to effectively uncover hidden patterns and relationships within large-scale social media datasets. This study introduces a conceptual framework for analyzing sentiment and engagement trends on social media by employing three-dimensional visual analytics and machine learning approaches. First, social media data is gathered and processed to eliminate noise, duplicates, and non-essential information. Sentiment-related characteristics are obtained using VADER and TextBlob, while engagement features are calculated based on interaction metrics including likes, shares, comments, and reposts. To tackle the difficulties posed by high-dimensional data, Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) are used to create compact three-dimensional representations that maintain essential structural information. Additionally, clustering algorithms such as K-Means and DBSCAN are employed to detect clusters of posts that share similar sentiment and engagement attributes. Techniques from network analysis are applied to explore interaction patterns, pinpoint influential users, and uncover online communities. The resulting three-dimensional visualizations enable an intuitive examination of user behavior, sentiment distribution, and engagement dynamics across social media platforms.
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