The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Abubakar Sadiq Muhammad, Muesser Nat
Informing Science: The International Journal of an Emerging Transdiscipline  •  Volume 25  •  2022  •  pp. 183-196

Nigeria’s university education goes through incessant strikes by the Academic Staff Union of Universities (ASUU). This strike has led to shared emotion on micro-blogging sites like Twitter. This study analyzed selected historical tweets from the “ASUU” to understand citizens’ opinions.

The researchers conducted sentiment analysis and topic modelling to understand Twitter users’ opinions on the strike.

The researchers used the Valence Aware Dictionary for Sentiment Reasoning (VADER) technique for sentiment analysis, and the Latent Dirichlet allocation (LDA) was used for topic modelling. A total of 10,000 tweets were first extracted for the study. After data cleaning, 1323 tweets were left.

To the researcher’s best knowledge, no published study has presented a sentiment analysis on the topic of the ASUU strike using the Twitter dataset. This research will fill this gap by providing a sentiment analysis and drawing out subjects by exploring the tweets on the phrase “ASUU.”

The sentiment analysis result using VADER returned 567 tweets as ‘Negative,’ with the remaining 544 and 212 categorized as Positive and Neutral. The result of the LDA returned six topics, all comprising seven keywords. The topics were the solution to the strike, ASUU strike effect, strike Call-off, appeal to ASUU, student protest and student appeal.

Researchers can use this study’s findings to compare with other contexts of opinion mining.

Practitioners may also use the research to understand better the attitudes of their staff and students about the strikes to create actionable solutions before the suspension of the strike.

Future studies can collect information from other social networking and blogging sites.

social media, ASUU, strike, opinion mining, sentiment analysis, topic modeling, Twitter
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