There are many open questions in this area of computer science that are very important from the perspective of the social media marketing. Among them is: "how to write the messages that are 'popular and liked'"? In this paper we will model and investigate one possible aspect of this issue: does sentiment of the social media post correlates with social engagement of fan base? We have modeled sentiment scoring of social media post using lexicon - based method and by state of the art convolutional neural network. The evaluation of those models has been performed using social media Twitter accounts of five worldknown politicians and celebrities, four brands, two bloggers and two users. We have investigated the various statistical dependencies between sentiment - based scores and engagement scores values. Basing on results we can concluded that number of favorites or shares (both are among the most popular engagement scoring methods that are present in most social media platforms) is not dependent on the sentiment of the message. It does not matter if posts have positive or negative sentiment. The results we have obtained are very important especially for researchers and business entities who utilizes social media platform. Large number of social media scoring algorithms utilizes some kind of binary sentiment analysis associated with social engagement scoring. Our results are strong indicators that two popular sentiment analysis methods should not be used as the predictors of mentioned social engagement scores. Our research can be easily reproduced because we publish both our data and source code of programs we used for evaluation.

There are many open questions in this area of computer science that are very important from the perspective of the social media marketing. Among them is: "how to write the messages that are 'popular and liked'"? In this paper we will model and investigate one possible aspect of this issue: does sentiment of the social media post correlates with social engagement of fan base? We have modeled sentiment scoring of social media post using lexicon - based method and by state of the art convolutional neural network. The evaluation of those models has been performed using social media Twitter accounts of five worldknown politicians and celebrities, four brands, two bloggers and two users. We have investigated the various statistical dependencies between sentiment - based scores and engagement scores values. Basing on results we can concluded that number of favorites or shares (both are among the most popular engagement scoring methods that are present in most social media platforms) is not dependent on the sentiment of the message. It does not matter if posts have positive or negative sentiment. The results we have obtained are very important especially for researchers and business entities who utilizes social media platform. Large number of social media scoring algorithms utilizes some kind of binary sentiment analysis associated with social engagement scoring. Our results are strong indicators that two popular sentiment analysis methods should not be used as the predictors of mentioned social engagement scores. Our research can be easily reproduced because we publish both our data and source code of programs we used for evaluation.