  This work investigates empirically the impact of political party control over
its candidates or vice versa on winning an election using a natural language
processing technique called sentiment analysis (SA). To do this, a set of 7430
tweets bearing or related to #AnambraDecides2017 was streamed during the
November 18, 2017, Anambra State gubernatorial election. These are Twitter
discussions on the top five political parties and their candidates termed
political actors in this paper. We conduct polarity and subjectivity sentiment
analyses on all the tweets considering time as a useful dimension of SA.
Furthermore, we use the word frequency to find words most associated with the
political actors in a given time. We find most talked about topics using a
topic modeling algorithm and how the computed sentiments and most frequent
words are related to the topics per political actor. Among other things, we
deduced from the experimental results that even though a political party serves
as a platform that sales the personality of a candidate, the acceptance of the
candidate/party adds to the winning of an election. For example, we found the
winner of the election Willie Obiano benefiting from the values his party share
among the people of the State. Associating his name with his party, All
Progressive Grand Alliance (APGA) displays more positive sentiments and the
subjective sentiment analysis indicates that Twitter users mentioning APGA are
less emotionally subjective in their tweets than the other parties.
