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This work investigates empirically the impact of political party control overits candidates or vice versa on winning an election using a natural languageprocessing technique called sentiment analysis (SA). To do this, a set of 7430tweets bearing or related to #AnambraDecides2017 was streamed during theNovember 18, 2017, Anambra State gubernatorial election. These are Twitterdiscussions on the top five political parties and their candidates termedpolitical actors in this paper. We conduct polarity and subjectivity sentimentanalyses on all the tweets considering time as a useful dimension of SA.Furthermore, we use the word frequency to find words most associated with thepolitical actors in a given time. We find most talked about topics using atopic modeling algorithm and how the computed sentiments and most frequentwords are related to the topics per political actor. Among other things, wededuced from the experimental results that even though a political party servesas a platform that sales the personality of a candidate, the acceptance of thecandidate/party adds to the winning of an election. For example, we found thewinner of the election Willie Obiano benefiting from the values his party shareamong the people of the State. Associating his name with his party, AllProgressive Grand Alliance (APGA) displays more positive sentiments and thesubjective sentiment analysis indicates that Twitter users mentioning APGA areless emotionally subjective in their tweets than the other parties.

The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets