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Title: Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts
Authors: Poecze, Flora 
Ebster, Claus 
Strauss, Christine 
Keywords: social media metrics;self-marketing;sentiment analysis
Issue Date: 2018
Publisher: Elsevier
Source: Procedia Computer Science, 130, 660-666
Journal: Procedia Computer Science 
Abstract: The present paper presents the results of an analysis of indicators underlying successful self-marketing techniques on social media. The participants included YouTube gamers. We focus on the content of their communication on Facebook to identify significant differences in terms of their user-generated Facebook metrics and commentary sentiments. Methodologically, ANOVA and sentiment analysis were applied. ANOVA of the classified post categories revealed that re-posted YouTube videos gained significantly fewer likes, comments, and shares from the audience. On the other hand, photos tended to show significantly more follower-generated actions compared to other post types in the sample. Sentiment analysis revealed underlying follower negativity when user-generated activity tended to be relatively low, offering valuable complementary results to the mere analysis of other post indicators, such as the number of likes, comments, and shares. The results indicated the necessity to utilize natural language processing techniques to optimize brand communication on social media and highlighted the importance of considering the opinion of the masses for better understanding of consumer feedback.
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.04.117
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:Wirtschaft (mit Schwerpunkt Zentral-Osteuropa)

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