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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

LINKPULSE ANALYTICS: LINKEDIN ENGAGEMENT AND SENTIMENT DASHBOARD

Yerrogolla Swathivika Nellutla Anjali Seemran Kumari Mohammed Anwar

P Niharika

Department of CSE(Data Science) ACE Engineering College Hyderabad Telangana India
DOI:https://doi.org/10.55041/ijcope.v2i4.169

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

LinkPulse Analytics is a data-driven dashboard which will help you analyze post performance on LinkedIn and Audience Engagement using analytical techniques and natural language processing. LinkPulse uses Key Engagement Metrics (like Likes, Comments, Impressions, Shares) to measure how effective your Content is at engaging with it's Audience. The dashboard provides an interactive interface built using Streamlit, and allows you to visualize how your Engagement metrics over time (which is a good way to identify Engagement trends and Performance patterns). Furthermore, in addition to using a quantitative approach, LinkPulse uses Sentiment Analysis to classify User comments into Positive, Negative, or Neutral Categories. Keywords extracted from User comments will be used to identify Frequently Discussed Themes and Audience Feedback. In addition, the dashboard can also be used to provide Competitive Benchmarking data across multiple LinkedIn Profiles, so you can compare your Engagement metrics against your Competitor's LinkedIn profiles. Overall, LinkPulse Analytics turns raw social Media data into actionable insights, which will help you optimise your Content Strategy and Improve Audience Engagement through Data Driven Decision Making.

How to Cite this Paper

Swathivika, Y., Anjali, N., Kumari, S. & Anwar, M. (2026). Linkpulse Analytics: Linkedin Engagement and Sentiment Dashboard. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.169

Swathivika, Yerrogolla, et al.. "Linkpulse Analytics: Linkedin Engagement and Sentiment Dashboard." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.169.

Swathivika, Yerrogolla,Nellutla Anjali,Seemran Kumari, and Mohammed Anwar. "Linkpulse Analytics: Linkedin Engagement and Sentiment Dashboard." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.169.

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References

[1] Smith et al. (2019) "Evaluating Social Media Interactions Using Statistical Techniques," published in the International Journal of Computer Applications, 175(8): 12-18

[2] Kumar and Sharma (2020) "Emotion Detection from Social Media Feedback Using Natural Language Processing," published in the International Journal of Engineering Research & Technology (IJERT) 9(4): 456-460

[3] Lee ,M., Port , S. and Kim , H. "Engagement Metrics for Evaluating LinkedIn Posting Engagement Data " Journal Page No. 2 No of 89 to 96 (New York, 2021) - Data Analytics Journal.

[4] Brown,T., Wilson , R. and Adam , K.` Use Of TF-IDF Methodology To Produce Keywords That Contribute Content Based search And Extraction From Social Media Posts" International Journal On Data Science Volume 6; 3 Issue , Page No 101 To 108 (New York 2021).

[5] Patel and Mehta published a paper in the International Journal of Advanced Research in Computer Science that provides an example of how to use a dashboard built with Streamlit and Python for the analysis of social media data.

[6] Singh and Rao published research in the Proceedings of the IEEE International Conference on Data Analytics about an approach to engagement and sentiment analysis that integrates multiple social media platforms.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 09 2026
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