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

Decision Making For Celebrity Branding: An Opinion Mining Approach Based  On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content  (CGC)