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.
Highlights
The online social networks, being a medium for communicating and sharing opinions, have provided us with a large and rich variety of facts, interests and opinions which can be accessed and mined, by leveraging on the strong predictive power of learning algorithms, to generate useful patterns about offline events [1] [2]
1) Profiles of the political parties a) All Progressive Congress (APC): APC became the largest political party in Nigeria by toppling the People’s Democratic Party (PDP) after an emphatic win at the national level in the 2015 Presidential election which brought in President Muhammadu Buhari
These results indicate that 52% of the public opinions expressed support for Atiku to emerge as the president of Nigeria while the remaining 48% goes to Buhari
Summary
The online social networks, being a medium for communicating and sharing opinions, have provided us with a large and rich variety of facts, interests and opinions which can be accessed and mined, by leveraging on the strong predictive power of learning algorithms, to generate useful patterns about offline events [1] [2]. Examples of such platforms include Twitter, Facebook, Instagram, YouTube among others. Public opinion mining comes handy and cogent in studies such as election predictions [6], crime analysis [7], Stock market analysis [8], consumers‟ behaviour [9], the outbreak of disease [10], public health [11] and so on
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