Numerous studies of social media analytics (SMA) shed light upon interesting insights into the information flow in social media. As social media becomes a crucial part of human society, bridging and merging these studies could shape ideas and designs for real-world applications that allow more transparency and understanding of social media. Among several challenges of SMA, this paper focuses on two issues of 1) invasive and greedy analysis methods concerning user privacy, and 2) lack of comprehensive representations of analysis results. We use our analysis on Telegram data to propose that pursuing persona profiling using generalizing contextual analysis via Natural Language Processing (NLP) technologies could address the first problem. For the second problem, we propose to visualize the analysis results, i.e. persona profiles, to increase both comprehensibility and interpretability.