Emotions are not just expressive but generative too

Writers used to express their emotions in letters, stories, or news articles, like for example "I love to be in the US". Well, writers can demonstrate their feelings, at the same time, they can spark off emotions in the reader's mind too. Established writers used to predict (upto some extend) their reader's view or response before publishing an article. How do they do this? What are the key factors they consider for a better response from the reader? Here I would like to share my thoughts regarding the prediction of the reader's emotions. Lets see some of the cases where posts (articles, books, tweets, or Facebook feeds) enables  the reader emotions.

i, If the reader feels that his/her emotions synchronize with the inline emotions of a post, obviously there is a higher chance to get positive feelings towards the writer and the post. At the same time conflicts in ideas or intellectual challenges may bring out the wrath of the reader.

ii, Here reader doesn't have much knowledge about the post, he/she is totally blank, for example if it is about some tragic incident that happened, then the post ends up as a fresh canvas, and can directly cross the reader's emotional boundary.

iii, Consider a novel, that may generate a wave of emotions from the beginning to the end. It will finally give a conclusive emotion to the reader.

iv, If the writer is a famous influencer (could be notorious too), he/she can easily attract more people. In this case fame is the factor.

v, If the post content itself is influential among the mob, it can also cross the emotional barriers of the mind. Here popularity of the post plays a big role.

All the above cases depend on certain factors such as reader's view, inline emotions in the post, reader's emotion towards the writer, fame of the writer, popularity of the post and the reader's emotion towards the topic of post. Predicting the readers mind and influencing it, is one of the biggest challenges of all time.

Predicting the reader's emotion using computation is very complex. But if it is possible, we can calculate the public influence rate for each and every tweet, Facebook post, news article and blog based on gender, location, profession and interests of the reader.