Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter

Fagbola, Temitayo Matthew; Thakur, Surendra Colin

Abstract

Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readily-available data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome. In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria. 224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API. tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes. Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election. Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel. Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP. Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari. These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election. These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election. Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events. Future research could investigate spatiotemporal dimensions of the prediction.

Citation

Fagbola, T. M., & Thakur, S. C. (2019). Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter. International journal of advanced computer science and applications : IJACSA, 10(10), 329-336. https://doi.org/10.14569/ijacsa.2019.0101047

• Journal Article Type: Article
• Publication Date: Oct 1, 2019
• Deposit Date: Jan 28, 2024
• Publicly Available Date: Feb 7, 2024
• Journal: International Journal of Advanced Computer Science and Applications
• Print ISSN: 2158-107X
• Electronic ISSN: 2156-5570
• Publisher: SAI Organization
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 10
• Pages: 329-336
• DOI: https://doi.org/10.14569/ijacsa.2019.0101047
• Keywords: Nigeria; 2019 presidential_election; botsawareness; EmoLex; lexicon_analysis; public_opinion; emotion_mining; sentiment_analysis; twitter; APC; PDP; win_prediction; muhammadu_buhari; atiku_abubaka
• Public URL: https://hull-repository.worktribe.com/output/4161542