Sentiment Analysis of Social Media Content for Music Recommendation

Authors

  • S Akuma Benue State University https://orcid.org/0000-0003-1909-7618
  • P Obilikwu Department of Mathematics and Computer Science, Benue State University, Makurdi, Nigeria
  • E Ahar Department of Mathematics and Computer Science, Benue State University, Makurdi, Nigeria

DOI:

: https://doi.org/10.46912/napas.225

Keywords:

Prediction, Emotion, Social media, Machine learning, Music recommendation, Sentiment analysis

Abstract

There is a growing use of social media for communication and entertainment. The information obtained from these social media platforms like Facebook, Linkedin, Twitter and so on can be used for inferring users’ emotional state. Users express their emotions on social media such as Twitter through text and emojis. Such expression can be harvested for the development of a recommender system. In this work, live tweets of users were harvested for the development of an emotion-based music recommender system. The emotions captured in this work include happy, fear, angry disgusted and sad. Users tweets in the form of emojis or text were matched with predefined variables to predict the emotion of users. Random testing of live tweets using the system was conducted and the result showed high predictability.

Published

2021-08-19

How to Cite

Akuma, S., Obilikwu, P., & Ahar, E. (2021). Sentiment Analysis of Social Media Content for Music Recommendation. NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES, 4(1), 95–102. https://doi.org/10.46912/napas.225