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Sentiment Analysis of Social Media Content for Music Recommendation
Author(s) -
Stephen Akuma,
PO Obilikwu,
E Ahar
Publication year - 2021
Publication title -
nigerian journals of pure and applied sciences (benue online)
Language(s) - English
Resource type - Journals
ISSN - 2705-3997
DOI - 10.46912/napas.225
Subject(s) - social media , entertainment , computer science , recommender system , sentiment analysis , predictability , emotion detection , world wide web , emotion recognition , artificial intelligence , art , physics , quantum mechanics , visual arts
There is a growing use of social media for communication and entertainment. The information obtained from these social media platforms like Facebook, Linkedln, 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.