Prediction of Social Media User’s Mood using Deep Learning

Authors

  • Sandhya MK Dept. of Computer Science and Engineering, Meenakshi Sundararajan Engineering College, Chennai, India
  • Soundarya V Dept. of Computer Science and Engineering, Meenakshi Sundararajan Engineering College, Chennai, India
  • Swarnalakshmi R Dept. of Computer Science and Engineering, Meenakshi Sundararajan Engineering College, Chennai, India
  • Swathi I Dept. of Computer Science and Engineering, Meenakshi Sundararajan Engineering College, Chennai, India

DOI:

https://doi.org/10.26438/ijcse/v6si3.113119

Keywords:

Mood Prediction, Social Media, Alert System, Time Critical Information, Depression, News Feed

Abstract

In recent times, there is a huge increase in the usage of social media to share one’s opinion, feelings and even daily activities. By predicting the mood of the users in social media, we can identify the users who discuss or express suicide-related information. Prediction of user’s mood based on the likes, shares and status posted by them on social media is a challenging task as the mood of users change frequently. In this paper, a scheme is proposed to predict the user’s mood based on the likes, shares and status posted in social media and identify the users in the state of depression. This scheme classifies the mood of user as happy, sad, neutral, angry etc. using deep learning. It presents news feeds to keep the depressed user happy and enthusiastic. When the user is in a prolonged state of depression, the alert system alerts the top five users in his/her friend list. This scheme predicts the mood of the users with accuracy around 87%. Further, time critical information is sent to some users who regularly share information such that it reaches all the users within a certain period of time.

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Published

2025-11-13
CITATION
DOI: 10.26438/ijcse/v6si3.113119
Published: 2025-11-13

How to Cite

[1]
M. Sandhya, V. Soundarya, R. Swarnalakshmi, and I. Swathi, “Prediction of Social Media User’s Mood using Deep Learning”, Int. J. Comp. Sci. Eng., vol. 6, no. 3, pp. 113–119, Nov. 2025.

Issue

Vol. 6 No. 3 (2018): IJCSE Special Issue April Edition

Section

Research Article

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