  Keyword extraction has received an increasing attention as an important
research topic which can lead to have advancements in diverse applications such
as document context categorization, text indexing and document classification.
In this paper we propose STF-IDF, a novel semantic method based on TF-IDF, for
scoring word importance of informal documents in a corpus. A set of nearly four
million documents from health-care social media was collected and was trained
in order to draw semantic model and to find the word embeddings. Then, the
features of semantic space were utilized to rearrange the original TF-IDF
scores through an iterative solution so as to improve the moderate performance
of this algorithm on informal texts. After testing the proposed method with 200
randomly chosen documents, our method managed to decrease the TF-IDF mean error
rate by a factor of 50% and reaching the mean error of 13.7%, as opposed to
27.2% of the original TF-IDF.
