Authors : Dr. R. P. Ambilwade

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/3k6ma7cj

DOI : https://doi.org/10.38124/ijisrt/25jul185

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The proliferation of social media platforms has created a complex landscape where understanding user sentiment and engagement is paramount for businesses. This study aims to decode digital emotions by analysing sentiment dynamics, engagement rates, and temporal patterns across major platforms. To achieve this, advanced sentiment analysis techniques and machine learning algorithms applied on a comprehensive dataset of 732 social media posts. The methodology involved refining sentiment labelling, parsing hashtags, extracting temporal features, calculating engagement metrics, and standardizing geolocation data. The results reveal significant variations in sentiment expression and engagement rates across platforms. The findings emphasize the importance of tailoring marketing strategies to platform- specific dynamics and user sentiment trends. This research provides a robust framework for leveraging sentiment analysis in strategic social media marketing, ultimately enhancing user engagement and brand visibility.

Keywords : Sentiment Analysis, Social Media Engagement, Platform Distribution, User Behaviour, Content Strategy, Digital Communication.

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