Machine Learning and Sentiment Analysis: Examining the Contextual Polarity of Public Sentiment on Malaria Disease in Social Networks

Oyelade, O. J. and Uwoghiren, Efosa and Isewon, Itunuoluwa and Oladipupo, O. O. and Aromolaran, Olufemi and Michael, Kingsley (2018) Machine Learning and Sentiment Analysis: Examining the Contextual Polarity of Public Sentiment on Malaria Disease in Social Networks. In: ISCA, BICOB.

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Abstract

Malaria, a major deadly disease which is still a threat to human life’s even though numerous efforts has been put to fight it, still affects over two hundred million people each year amongst which over a million individuals dies. Twitter happens to be an important and comprehensive source of information that is quite subjective to individual sentiments towards public health care. In this study, we extracted tweets from the social network twitter, we pre-processed the tweets extracted and built a model to fit our data using a machine learning approach for text classification to determine the contextual polarity of every tweet on the subject of malaria in the bid to harvest peoples’ opinion towards malaria and understand how well research and recent development in the aid to tackle malaria has affected the opinions of the public towards the subject malaria. This study finds that tweets extracted, pre-processed and classified in this study were majorly classified as negative (-ve) due to the fact that tweets tweeted were majorly about different occurrence of death, misinformation and need for donations to save a life, hence a major awareness is needed.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Sentiment Analysis, Machine Learning Technique, Malaria, Twitter, Data Mining.
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Hannah Akinwumi
Date Deposited:	21 Jun 2021 16:09
Last Modified:	21 Jun 2021 16:09
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/14812

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