KLVR Saraswathi¹*, K Bhanu Charan1, J Yogendra¹ and K Kranthi Kumar²

¹Students of Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
²Faculty of Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India

*Corresponding Author: KLVR Saraswathi, Students of Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.

Received: May 06, 2024; Published: July 17, 2024

Abstract

This project aims the challenge of navigating social media by categorizing trending topics like cinema, crime, education, business, and sports. Unlike conventional methods, we use machine learning and natural language processing to explore deeper into insights. By precisely categorizing topics and visualizing them based on their content and context, users can effortlessly discover relevant information. Our approach offers a clear pathway to the discussions that capture users' interest the most. Conventional systems often rely solely on sentiment analysis, overlooking context and precision. Our innovative approach presents a more sophisticated solution for social media analysis. Users can swiftly access the latest movie buzz, educational trends, or business insights with assurance. Through advanced algorithms and visualization techniques, we ensure the precise categorization of trending topics. Our project empowers users to make informed decisions and uncover valuable insights from social media.

Keywords: Social Media; Trending Topics; Categorization; Visualisation; Machine Learning; Natural Language Processing; Clustering

References

1. Bitner M J and Zeithaml V A. “Technology's Impact on the Gaps Model of Service Quality”. (2010).
2. Kapoor KK and Tamilmani K. “Advances in Social Media Research: Past, Present, and Future”. Springer (2017).
3. Baatarjav EA and Dantu R. “Unveiling Hidden Patterns to Find Social Relevance”. (2011).
4. Chen PL., et al. “Analysis of Social Media Data: An Introduction to the Characteristics and Chronological Process”. Springer (2018).

Citation

Citation: KLVR Saraswathi., et al. “A Novel Method for Social Media Trend Analysis Using Categorization of Posts".Acta Scientific Computer Sciences 6.8 (2024): 11-18.

Copyright

Copyright: © 2024 KLVR Saraswathi., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.