Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

Identifying the Influences Behind the LinkedIn Posts using Topic Modeling and Sentiment Analysis

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343215,
        author={Nagaraj R and Rohith Adithya C R and Sakalabathula Sri Chakra Teja and Deepika T},
        title={Identifying the Influences Behind the LinkedIn Posts using Topic Modeling and Sentiment Analysis},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={social networking linkedin topic modeling sentiment analysis},
        doi={10.4108/eai.23-11-2023.2343215}
    }
    
  • Nagaraj R
    Rohith Adithya C R
    Sakalabathula Sri Chakra Teja
    Deepika T
    Year: 2024
    Identifying the Influences Behind the LinkedIn Posts using Topic Modeling and Sentiment Analysis
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343215
Nagaraj R1,*, Rohith Adithya C R1, Sakalabathula Sri Chakra Teja1, Deepika T1
  • 1: Department of Computer Science Engineering, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, India
*Contact email: rnagaraj3004@gmail.com

Abstract

The ubiquity of social networking has transformed daily life. People constantly share opinions, rate products, and conduct business on these platforms. Social media is vital in business, helping establish connections with prospects and clients. LinkedIn, specifically, excels in building trust through success stories, promotions, and recommendations. It connects professionals worldwide with the general public. However, only a few posts gain significant influence, and the factors behind this remain unknown. To address this, we propose a solution involving topic modeling and sentiment analysis. Our project aims to uncover the influences behind LinkedIn posts by scrutinizing media content, utilizing natural language processing techniques, and identifying sub-topics and aspects through topic modeling and sentiment analysis.