Social media makes it possible for anyone to share and transmit their opinions and sentiments to the rest of the world via the Internet. Recently, sentiment analysis is being used to investigate the opinions posted on Online Social Networks(OSN) based on multiple inputs, such as user profile characteristics, slang, emoticons, among others. However, the current sentiment analysis tools do not consider the influence of tagging people on OSN. In this context, this paper analyzes the impact of the tagging parameter on the global sentiment score of a text. The experimental results of subjective tests show that a correction factor must be considered in case of tagging people. Experimental results demonstrate that the tagging parameter affects the sentiment intensity value, in different ways, depending on the gender of the person who wrote the text and the sentiment polarity of the text. The new sentiment intensity metric considering the tagging people parameter reaches a Pearson Correlation Coefficient of 0.93 and a maximum error of 0.07, for texts of negative polarity written by women, at a 5-point scale. Furthermore, a mobile application with the new sentiment metric, which considers the tagging parameter, is built.