  The volume of discussions concerning brands within social media provides
digital marketers with great opportunities for tracking and analyzing the
feelings and views of consumers toward brands, products, influencers, services,
and ad campaigns in CGC. The present study aims to assess and compare the
performance of firms and celebrities (i.e., influencers that with the
experience of being in an ad campaign of those companies) with the automated
sentiment analysis that was employed for CGC at social media while exploring
the feeling of the consumers toward them to observe which influencer (of two
for each company) had a closer effect with the corresponding corporation on
consumer minds. For this purpose, several consumer tweets from the pages of
brands and influencers were utilized to make a comparison of machine learning
and lexicon-based approaches to the sentiment analysis through the Naive
algorithm (lexicon-based) and Naive Bayes algorithm (machine learning method)
and obtain the desired results to assess the campaigns. The findings suggested
that the approaches were dissimilar in terms of accuracy; the machine learning
method yielded higher accuracy. Finally, the results showed which influencer
was more appropriate according to their existence in previous campaigns and
helped choose the right influencer in the future for our company and have a
better, more appropriate, and more efficient ad campaign subsequently. It is
required to conduct further studies on the accuracy improvement of the
sentiment classification. This approach should be employed for other social
media CGC types. The results revealed decision-making for which sentiment
analysis methods are the best approaches for the analysis of social media. It
was also found that companies should be aware of their consumers' sentiments
and choose the right person every time they think of a campaign.
